!pip install xgboost

Requirement already satisfied: xgboost in /opt/anaconda3/lib/python3.11/site-packages (2.1.4)
Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.11/site-packages (from xgboost) (1.26.4)
Requirement already satisfied: scipy in /opt/anaconda3/lib/python3.11/site-packages (from xgboost) (1.10.1)

# Installing the sentence-transformers and gensim libraries for word embeddings
!pip install -U sentence-transformers gensim transformers tqdm -q

# Install llama-cpp-python if not already installed
%pip install llama-cpp-python

Requirement already satisfied: llama-cpp-python in /opt/anaconda3/lib/python3.11/site-packages (0.3.6)
Requirement already satisfied: typing-extensions>=4.5.0 in /opt/anaconda3/lib/python3.11/site-packages (from llama-cpp-python) (4.9.0)
Requirement already satisfied: numpy>=1.20.0 in /opt/anaconda3/lib/python3.11/site-packages (from llama-cpp-python) (1.26.4)
Requirement already satisfied: diskcache>=5.6.1 in /opt/anaconda3/lib/python3.11/site-packages (from llama-cpp-python) (5.6.3)
Requirement already satisfied: jinja2>=2.11.3 in /opt/anaconda3/lib/python3.11/site-packages (from llama-cpp-python) (3.1.3)
Requirement already satisfied: MarkupSafe>=2.0 in /opt/anaconda3/lib/python3.11/site-packages (from jinja2>=2.11.3->llama-cpp-python) (2.1.3)
Note: you may need to restart the kernel to use updated packages.

# Installation for GPU llama-cpp-python
# uncomment and run the following code in case GPU is being used
!CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python -q

# Installation for CPU llama-cpp-python
# uncomment and run the following code in case GPU is not being used
# !CMAKE_ARGS="-DLLAMA_CUBLAS=off" FORCE_CMAKE=1 pip install llama-cpp-python -q

# Import the google drive library to read the datasets from the google drive
#from google.colab import drive
#drive.mount("/content/drive")

# Import markdown and display from IPython to display the output in markdown.
import IPython
from IPython.display import display, Markdown

# Get the version of IPython package
display(Markdown("**Observations:**\n- The version of IPython is {}.".format(IPython.__version__)))

# Import numpy as np
import numpy as np

# Get the version of numpy
display(Markdown("**Observations:**\n- The version of numpy is {}.".format(np.__version__)))

# Import the pandas library to load the data into dataframe
import pandas as pd

# Get the version of Pandas
display(Markdown("**Observations:**\n- The version of pandas is {}.".format(pd.__version__)))

# Import seaborn and matplotlib.pyplot to plot the graphs
import seaborn as sns
import matplotlib.pyplot as plt

%matplotlib inline

# Get the version of seaborn
display(Markdown("**Observations:**\n- The version of seaborn is {}.".format(sns.__version__)))

# Import sklearn
import sklearn

# Import model_selection.train_test_split()
from sklearn.model_selection import train_test_split

# Import DecisionTreeClassifier from sklearn.tree
from sklearn.tree import DecisionTreeClassifier

# Import RandomForestClassifier from sklearn.ensemble
from sklearn.ensemble import RandomForestClassifier

# Import AdaboostClassifier from sklearn.ensemble
from sklearn.ensemble import AdaBoostClassifier

# Import GradientBoostingClassifier from sklearn.ensemble
from sklearn.ensemble import GradientBoostingClassifier

# Import Encoder from sklearn.ensemble
from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()

# Import accuracy_score, recall_score, precision_score, f1_score
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score

# Import GridSearchCV from sklearn.model_selection
from sklearn.model_selection import GridSearchCV

# Get the version of sklearn
display(Markdown("**Observations:**\n- The version of sklearn is {}.".format(sklearn.__version__)))

# Import regular expression library
import re

# Get the version of regular expression
display(Markdown("**Observations:**\n- The version of regular expression is {}.".format(re.__version__)))

# Import NLTK
import nltk

# Download the stopwords
nltk.download("stopwords")

# Download the wordnet to perform stemming
nltk.download('wordnet')

# Import the stopwords to remove common stop words
from nltk.corpus import stopwords

# Import the PorterStemmer from nltk.stem.porter to perform stemming
from nltk.stem.porter import PorterStemmer

# Create an instance for PorterStemmer and this will be used to perform stemming
stemmer = PorterStemmer()

# Get the version of nltk
display(Markdown("**Observations:**\n- The version of nltk is {}.".format(nltk.__version__)))

[nltk_data] Downloading package stopwords to
[nltk_data]     /Users/vinayakhampiholi/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package wordnet to
[nltk_data]     /Users/vinayakhampiholi/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!

# Import gensim
import gensim

# Import Word2Vec from gensim.models
from gensim.models import Word2Vec

# Import glove2word2vec from gensim.scripts.glove2word2vec
from gensim.scripts.glove2word2vec import KeyedVectors

# Get the filename
glove_file = "glove.6B.100d.txt.word2vec"

# Get the version of gensim
display(Markdown("**Observations:**\n- The version of gensim is {}.".format(nltk.__version__)))

# Import sentence_transformers
import sentence_transformers

# Import SentenceTransformer from sentence_transformers
from sentence_transformers import SentenceTransformer

# Get the version of sentence_transformers
display(Markdown("**Observations:**\n- The version of sentence_transformers is {}.".format(sentence_transformers.__version__)))

# Import xgboost
import xgboost

# Import XGBClassifier from xgboost
from xgboost import XGBClassifier

# Get the version of xgboost
display(Markdown("**Observations:**\n- The version of sentence_transformers is {}.".format(xgboost.__version__)))

# Import torch for Deep Learning library
import torch

# Display the version of the torch
display(Markdown("**Observations:**\n- The version of torch is {}.".format(torch.__version__)))

# Function to download the model from the Hugging Face model hub
from huggingface_hub import hf_hub_download

# Importing the Llama class from the llama_cpp module
from llama_cpp import Llama

# For progress bar related functionalities
from tqdm import tqdm

# To use the progress bar
tqdm.pandas()

# Import warnings
import warnings

# Ignore all warnings
warnings.filterwarnings("ignore")

# Create a function that removes the special characters from the input text
def remove_special_characters(text):

  # Defining the regex pattern to match non-alphanumeric characters
  pattern = "[^A-Za-z0-9]+"

  # Finding the specified pattern and replacing non-alphanumeric characters with a blank string
  new_text = "".join(re.sub(pattern, " ", text))

  # Return the new_text
  return new_text

# Create a function that removes the stopwords from the input text using the nltk.stopwords()
def remove_stopwords(text):

  # Splitting the text into words
  words = text.split()

  # Removing English language stopwords
  new_text = " ".join([word for word in words if word not in stopwords.words("english")])

  # Return the new_text
  return new_text

# Stemming is a language processing method that chops off word endings to find the root or base form of words. E.g, converting Jumping to Jump or Running to Run etc.

# Create a function that performs the described stemming operations using PorterStemmer
def apply_porter_stemmer(text):

  # Split text into separate words
  words = text.split()

  # Applying the Porter Stemmer on every word of a message and joining the stemmed words back into a single string
  new_text = " ".join([stemmer.stem(word) for word in words])

  # Return the new_text
  return new_text

# Create a function to vectorize the vectors
def average_vectorizer_Word2Vec(doc):

  # Initializing a feature vector for the sentence
  feature_vector = np.zeros((vec_size,), dtype="float64")

  # Creating a list of words in the sentence that are present in the model vocabulary
  words_in_vocab = [word for word in doc.split() if word in words]

  # adding the vector representations of the words
  for word in words_in_vocab:
    feature_vector += np.array(word_vector_dict[word])

  # Dividing by the number of words to get the average vector
  if len(words_in_vocab) != 0:
    feature_vector /= len(words_in_vocab)

  # Return feature_vector
  return feature_vector

# Create a function to vectorize the vectors
def average_vectorizer_GloVe(doc):

  # Initializing a feature vector for the sentence
  feature_vector = np.zeros((vec_size,), dtype="float64")

  # Creating a list of words in the sentence that are present in the model vocabulary
  words_in_vocab = [word for word in doc.split() if word in glove_words]

  # adding the vector representations of the words
  for word in words_in_vocab:
    feature_vector += np.array(glove_word_vector_dict[word])

  # Dividing by the number of words to get the average vector
  if len(words_in_vocab) != 0:
    feature_vector /= len(words_in_vocab)

  # Return feature_vector
  return feature_vector

# Create a function to compute the cosine similarity between two embedding vectors
def cosine_score(model, text):
  # encoding the text
  embeddings = model.encode(text)

  # calculating the L2 norm of the embedding vector
  norm1 = np.linalg.norm(embeddings[0])
  norm2 = np.linalg.norm(embeddings[1])

  # computing the cosine similarity
  cosine_similarity_score = ((np.dot(embeddings[0],embeddings[1]))/(norm1*norm2))

  return cosine_similarity_score

# Create a function that returns the Performance Metrics of a model
def model_performance_classification_report(model, predictors, target, model_name):

  # Make predictions using the classifier
  predions = model.predict(predictors)

  # Compute Accuracy
  accuracy = accuracy_score(target, predions)

  # Compute Recall
  recall = recall_score(target, predions, average = "weighted")

  # Compute Precision
  precision = precision_score(target, predions, average = "weighted")

  # Compute F1-score
  f1 = f1_score(target, predions, average = "weighted")

  # Create a DataFrame to store the metrics
  performance_dataframe = pd.DataFrame({"Accuracy": [accuracy], "Recall": [recall], "Precision": [precision], "F1": [f1], "Model": model_name})

  # Return the dataframe
  return performance_dataframe

# Create a function that returns the confusion matrix
def confusion_matrix(model, predictors, target, model_name):

  # Make predictions using the classifier
  predictions = model.predict(predictors)

  # Define the labels for the confusion matrix
  labels = ["Negative", "Neutral", "Positive"]

  # Compute the confusion matrix
  confusion_matrix = pd.crosstab(target, predictions)

  # Plot the confusion matrix using a heatmap with annotations
  sns.heatmap(confusion_matrix, annot = True, fmt = "d", cmap = "Blues", xticklabels = labels, yticklabels =labels)

  # Label for the x-axis
  plt.xlabel("Predicted")

  # Label for the y-axis
  plt.ylabel("Actual")

  # Set the title of the plot
  plt.title("Confusion Matrix " + model_name)

  # Display the plot
  plt.show()

  # Return the confusion matrix
  return confusion_matrix

# defining a function to parse the JSON output from the model
def extract_json_data(json_str):
    import json
    try:
        # Find the indices of the opening and closing curly braces
        json_start = json_str.find('{')
        json_end = json_str.rfind('}')

        if json_start != -1 and json_end != -1:
            extracted_category = json_str[json_start:json_end + 1]  # Extract the JSON object
            data_dict = json.loads(extracted_category)
            return data_dict
        else:
            print(f"Warning: JSON object not found in response: {json_str}")
            return {}
    except json.JSONDecodeError as e:
        print(f"Error parsing JSON: {e}")
        return {}

#Defining the response function
def response_mistral_1(prompt, news):
    model_output = llm(
      f"""
      [INST]
      {prompt}
      News Articles: {news}
      [/INST]
      """,
      max_tokens=1024,      # Set the maximum number of tokens the model should generate for this task.
      temperature=0.7,     # Set the value for temperature.
      top_p=0.9,           # Set the value for top_p.
      top_k=50,            # Set the value for top_k.
      echo=False,
    )

    final_output = model_output["choices"][0]["text"]

    return final_output

# We will load the data into a raw dataframe and use a copy of the dataframe for further analysis
stock_news_raw = pd.read_csv("stock_news.csv")
stock_news = stock_news_raw.copy()

# Display the status of the data loading
display(Markdown("**Observations:**"))
if stock_news is None:
    display(Markdown("- The data loading is not successful."))
else:
    display(Markdown("- The data is loaded successfully."))

# Print the first 5 rows of the dataset
stock_news.head(5)

# Show the rows and column details of the dataset
display(Markdown("**Observations:**"))

# Show the number of rows
display(Markdown("- The number of rows in the dataset: {}.".format(stock_news.shape[0])))

# Show the number of columns
display(Markdown("- The number of cols in the dataset: {}.".format(stock_news.shape[1])))

# Show the datatypes of the columns
stock_news.info()

# Provide observations on the above information
display(Markdown("**Observations:**"))

# Display the datatype for each column
for col in stock_news.columns:

  # Display the column name and datatype
  display(Markdown("- The datatype of {} is {}.".format(col, stock_news[col].dtype)))

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 349 entries, 0 to 348
Data columns (total 8 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Date    349 non-null    object 
 1   News    349 non-null    object 
 2   Open    349 non-null    float64
 3   High    349 non-null    float64
 4   Low     349 non-null    float64
 5   Close   349 non-null    float64
 6   Volume  349 non-null    int64  
 7   Label   349 non-null    int64  
dtypes: float64(4), int64(2), object(2)
memory usage: 21.9+ KB

# Check the null values
display(Markdown("**Observations:**"))

# Display the null value information
display(Markdown("- The number of null values in the dataset: {}.".format(stock_news.isnull().sum().sum())))

# Check the duplicate rows in the data set
display(Markdown("**Observations:**"))

# Display the duplicate row information
display(Markdown("- The number of duplicate rows in the dataset: {}.".format(stock_news.duplicated().sum())))

# The dataset has an onbject column Date. This contains the dates and we can convert the datatype to date.
stock_news["Date"] = pd.to_datetime(stock_news["Date"])

# Display the information of the columns
stock_news.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 349 entries, 0 to 348
Data columns (total 8 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   Date    349 non-null    datetime64[ns]
 1   News    349 non-null    object        
 2   Open    349 non-null    float64       
 3   High    349 non-null    float64       
 4   Low     349 non-null    float64       
 5   Close   349 non-null    float64       
 6   Volume  349 non-null    int64         
 7   Label   349 non-null    int64         
dtypes: datetime64[ns](1), float64(4), int64(2), object(1)
memory usage: 21.9+ KB

# Print the unique values for each non-object column
stock_news.nunique()

Date       71
News      349
Open       70
High       70
Low        71
Close      71
Volume     71
Label       3
dtype: int64

# Perform the 5 point statistical summary on the dataset
stock_news.describe().transpose()

# Draw the count plot for the column Label
sns.countplot(data = stock_news, x = "Label")

# Set the title
plt.title("Distribution of Label")

# Display the countplot
plt.show()

# Create a mapping for the sentiment labels
sentiment_mapping = {0: 'Neutral', 1: 'Positive', -1: 'Negative'}

# Map the sentiment labels to the DataFrame
stock_news["Label_Meaning"] = stock_news["Label"].map(sentiment_mapping)

# Display the 10 rows to validate
stock_news.head(10)

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# Draw the histplot for the column Open
sns.histplot(data = stock_news, x = "Open", hue = "Label_Meaning", kde = True)

# Set the title
plt.title("Distribution of Open")

# Tilt the x axis to 90 degrees
plt.xticks(rotation = 90)

# Display the histplot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# Draw the boxplot for the column Open
sns.boxplot(data = stock_news, x = "Open", hue = "Label_Meaning")

# Set the title
plt.title("Distribution of Open")

# Tilt the values of x axis to 90 degrees
plt.xticks(rotation = 90)

# Display the boxplot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# Draw the histplot for the column High
sns.histplot(data = stock_news, x = "High", hue = "Label_Meaning", kde = True)

# Set the title
plt.title("Distribution of High")

# Tilt the values of x axis to 90 degrees
plt.xticks(rotation = 90)

# Display the histplot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# Draw the boxplot for the column Close
sns.boxplot(data = stock_news, x = "High", hue = "Label_Meaning")

# Set the title
plt.title("Distribution of High")

# Tilt the values of x axis to 90 degrees
plt.xticks(rotation = 90)

# Display the boxplot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# Draw the histplot for the column Low
sns.histplot(data = stock_news, x = "Low", hue = "Label_Meaning", kde = True)

# Set the title
plt.title("Distribution of Low")

# Tilt the values of x axis to 90 degrees
plt.xticks(rotation = 90)

# Display the histplot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# Draw the boxplot for the column Low
sns.boxplot(data = stock_news, x = "Low", hue = "Label_Meaning")

# Set the title
plt.title("Distribution of Low")

# Tilt the values of x axis to 90 degrees
plt.xticks(rotation = 90)

# Display the boxplot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# Draw the histplot for the column Close
sns.histplot(data = stock_news, x = "Close", hue = "Label_Meaning", kde = True)

# Set the title
plt.title("Distribution of Close")

# Tilt the values of x axis to 90 degrees
plt.xticks(rotation = 90)

# Display the histplot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# Draw the boxplot for the column Close
sns.boxplot(data = stock_news, x = "Close", hue = "Label_Meaning")

# Set the title
plt.title("Distribution of Close")

# Tilt the values of x axis to 90 degrees
plt.xticks(rotation = 90)

# Display the boxplot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# Draw the histplot for the volume column
sns.histplot(data = stock_news, x = "Volume", hue = "Label_Meaning", kde = True)

# Set the title
plt.title("Distribution of Volume")

# Display the histplot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# Draw the boxplot for the volumne column
sns.boxplot(data = stock_news, x = "Volume", hue = "Label_Meaning")

# Set the title
plt.title("Distribution of Volume")

# Tilt the x axis to 90 degrees
plt.xticks(rotation = 90)

# Display the boxplot
plt.show()

# Get the number of characters in the News column and show the distribution of the length of the sentiment
stock_news["News_Length"] = stock_news["News"].apply(lambda x: len(x))

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# show the new column News_Length in te distplot
sns.histplot(data = stock_news, x = "News_Length", hue = "Label_Meaning", kde = True)

# Set the title
plt.title("Distribution of News Length")

# Tilt the values of x axis to 90 degrees
plt.xticks(rotation = 90)

# Display the plot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (20, 10))

# Draw the boxplot for the above column News_lenght
sns.boxplot(data = stock_news, x = "News_Length", hue = "Label_Meaning")

# Set the title
plt.title("Distribution of News Length")

# Tilt the values of x axis to 90 degrees
plt.xticks(rotation = 90)

# Display the boxplot
plt.show()

# Use all the above columns Open, Close, High and Low and plot the distribution plot
sns.displot(data = stock_news[['Open','Close','High','Low']], kind = "kde")

# Display the plot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (40, 10))

# Use the Data in x axis and count of each Label in Y axis with the same column as hue
sns.countplot(data = stock_news, x = "Date", hue = "Label_Meaning")

# Set the title
plt.title("Label Vs Date")

# Tilt the x values to 90 degress for better visibility
plt.xticks(rotation = 90)

# Display the plot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (40, 10))

# Draw a lineplot for Open vs Date values
sns.lineplot(data = stock_news, x = "Date", y = "Open", hue = "Label_Meaning")

# Set the title
plt.title("Open Vs Date")

# Tilt the x values to 90 degress for better visibility
plt.xticks(rotation = 90)

# Display the plot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (40, 10))

# Draw a lineplot for High vs Date values
sns.lineplot(data = stock_news, x = "Date", y = "High", hue = "Label_Meaning")

# Set the title
plt.title("High Vs Date")

# Tilt the x values to 90 degress for better visibility
plt.xticks(rotation = 90)

# Display the plot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (40, 10))

# Draw a lineplot for Low vs Date values
sns.lineplot(data = stock_news, x = "Date", y = "Low", hue = "Label_Meaning")

# Set the title
plt.title("Low Vs Date")

# Tilt the x values to 90 degress for better visibility
plt.xticks(rotation = 90)

# Display the plot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (40, 10))

# Draw a lineplot for Close vs Date values
sns.lineplot(data = stock_news, x = "Date", y = "Close", hue = "Label_Meaning")

# Set the title
plt.title("Close Vs Date")

# Tilt the x values to 90 degress for better visibility
plt.xticks(rotation = 90)

# Display the plot
plt.show()

# Use a larger graph with a huge width to involve all the dates.
plt.figure(figsize = (40, 10))

# Draw a lineplot for Volumne vs Date values
sns.lineplot(data = stock_news, x = "Date", y = "Volume", hue = "Label_Meaning")

# Set the title
plt.title("Volume Vs Date")

# Tilt the x values to 90 degress for better visibility
plt.xticks(rotation = 90)

# Display the plot
plt.show()

# Draw a lineplot for Open vs High values
sns.lineplot(data = stock_news, x = "Open", y = "High", hue = "Label_Meaning")

# Set the title
plt.title("High Vs Open")

# Display the plot
plt.show()

# Draw a lineplot for Open vs Low values
sns.lineplot(data = stock_news, x = "Open", y = "Low", hue = "Label_Meaning")

# Set the title
plt.title("Low Vs Open")

# Display the plot
plt.show()

# Draw a lineplot for Close vs Open values
sns.lineplot(data = stock_news, x = "Open", y = "Close", hue = "Label_Meaning")

# Set the title
plt.title("Close Vs Open")

# Display the plot
plt.show()

# Draw a lineplot for Open vs Volume values
sns.lineplot(data = stock_news, x = "Open", y = "Volume", hue = "Label_Meaning")

# Set the title
plt.title("Volume Vs Open")

# Display the plot
plt.show()

# Draw a lineplot for High vs News_Length values
sns.lineplot(data = stock_news, x = "Open", y = "News_Length", hue = "Label_Meaning")

# Set the title
plt.title("News_Length Vs Open")

# Display the plot
plt.show()

# Draw a lineplot for Low vs High values
sns.lineplot(data = stock_news, x = "High", y = "Low", hue = "Label_Meaning")

# Set the title
plt.title("Low Vs High")

# Display the plot
plt.show()

# Draw a lineplot for Close vs High values
sns.lineplot(data = stock_news, x = "High", y = "Close", hue = "Label_Meaning")

# Set the title
plt.title("Close Vs High")

# Display the plot
plt.show()

# Draw a lineplot for Volume vs High values
sns.lineplot(data = stock_news, x = "High", y = "Volume", hue = "Label_Meaning")

# Set the title
plt.title("Volume Vs High")

# Display the plot
plt.show()

# Draw a lineplot for News_Length vs High values
sns.lineplot(data = stock_news, x = "High", y = "News_Length", hue = "Label_Meaning")

# Set the title
plt.title("News_Lenght Vs High")

# Display the plot
plt.show()

# Draw a lineplot for Close vs Low values
sns.lineplot(data = stock_news, x = "Low", y = "Close", hue = "Label_Meaning")

# Set the title
plt.title("Close Vs Low")

# Display the plot
plt.show()

# Draw a lineplot for Volume Vs Low values
sns.lineplot(data = stock_news, x = "Low", y = "Volume", hue = "Label_Meaning")

# Set the title
plt.title("Volume Vs Low")

# Display the plot
plt.show()

# Draw a lineplot for News_Length vs Low values
sns.lineplot(data = stock_news, x = "Low", y = "News_Length", hue = "Label_Meaning")

# Set the title
plt.title("News_Length Vs Low")

# Display the plot
plt.show()

# Draw a lineplot for Volume vs Close values
sns.lineplot(data = stock_news, x = "Close", y = "Volume", hue = "Label_Meaning")

# Set the title
plt.title("Volume Vs Close")

# Display the plot
plt.show()

# Draw a lineplot for News_Lenght vs Close values
sns.lineplot(data = stock_news, x = "Close", y = "News_Length", hue = "Label_Meaning")

# Set the title
plt.title("News_Length Vs Close")

# Display the plot
plt.show()

# Draw a lineplot for News_Length vs Volume values
sns.lineplot(data = stock_news, x = "Volume", y = "News_Length", hue = "Label_Meaning")

# Set the title
plt.title("News_Length Vs Volume")

# Display the plot
plt.show()

# Create a pairplot between Open, High, Low, Close, Volume and News_Length attributes
sns.pairplot(data = stock_news[["Open", "High", "Low", "Close", "Volume", "News_Length"]])

# Set the title
plt.title("Pairplot for Open, High, Low, Close, Volume and News_Length")

# Display the plot
plt.show()

# Create the heatmap for the correlation matrix of attributes Open, High, Low, Close and Volume
sns.heatmap(data = stock_news[["Open", "High", "Low", "Close", "Volume", "News_Length"]].corr(), annot = True)

# Display the plot
plt.show()

# Print the 10 rows of the dataframe to identify the possible data transformations
stock_news.head(10)

# Get the column information to decide the column data transformation strategy.
stock_news.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 349 entries, 0 to 348
Data columns (total 10 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   Date           349 non-null    datetime64[ns]
 1   News           349 non-null    object        
 2   Open           349 non-null    float64       
 3   High           349 non-null    float64       
 4   Low            349 non-null    float64       
 5   Close          349 non-null    float64       
 6   Volume         349 non-null    int64         
 7   Label          349 non-null    int64         
 8   Label_Meaning  349 non-null    object        
 9   News_Length    349 non-null    int64         
dtypes: datetime64[ns](1), float64(4), int64(3), object(2)
memory usage: 27.4+ KB

# Extract Day from the Date attribute and store it in Day attribute
stock_news["Day"] = stock_news["Date"].dt.day

# Extract Month from the Date attribute and store it in Month attribute
stock_news["Month"] = stock_news["Date"].dt.month

# Extract Year from the Date attribute and store it in Year attribute
stock_news["Year"] = stock_news["Date"].dt.year

# Display the 10 rows to validate
stock_news.head(10)

# Remove the special characters from News Column
stock_news["News_Cleaned"] = stock_news["News"].apply(remove_special_characters)

# Distplay the first 10 rows to validate the data
stock_news.head(10)

# changing the case of the News_Cleaned data to lower case
stock_news["News_Cleaned"] = stock_news["News_Cleaned"].str.lower()

# Distplay the first 10 rows to validate the data
stock_news.head(10)

# removing extra whitespaces from the News_Cleaned
stock_news["News_Cleaned"] = stock_news["News_Cleaned"].str.strip()

# Distplay the first 10 rows to validate the data
stock_news.head(10)

# remove stopwords from the News_Cleaned using remove_stopwords
stock_news["News_Cleaned"] = stock_news["News_Cleaned"].apply(remove_stopwords)

# Distplay the first 10 rows to validate the data
stock_news.head(10)

# Perform the stemming operations on News_Cleaned using apply_porter_stemmer function
stock_news["News_Cleaned"] = stock_news["News_Cleaned"].apply(apply_porter_stemmer)

# Distplay the first 10 rows to validate the data
stock_news.head(10)

# Check the distribution of the target value Label in order to maintain the proportionate distribution
stock_news["Label"].value_counts(normalize = False)

 0    170
-1     99
 1     80
Name: Label, dtype: int64

# Split the data into 70% Train and 20% Temp (Validation + Test)
train_data, temp_data, train_labels, temp_labels = train_test_split(stock_news.drop(columns = ["Label", "Label_Meaning"], axis = 1), stock_news["Label"], test_size = 0.3, stratify = stock_news["Label"], random_state = 42)

# Split the temp_data further into 10% Validation and 10% Test
valid_data, test_data, valid_labels, test_labels = train_test_split(temp_data, temp_labels, test_size = 0.5, stratify = temp_labels, random_state = 42)

# Display the stats of the train, valid and test data
display(Markdown("**Observations:**"))

# Print sizes of splits
display(Markdown("- Train Size: {}, Validation Size: {}, Test Size: {}".format(len(train_data), len(valid_data), len(test_data))))

# Diplay the distribution of values of Label in train_labels
train_labels.value_counts(normalize = True)

 0    0.487705
-1    0.282787
 1    0.229508
Name: Label, dtype: float64

# Diplay the distribution of values of Label in valid_labels
valid_labels.value_counts(normalize = True)

 0    0.480769
-1    0.288462
 1    0.230769
Name: Label, dtype: float64

# Diplay the distribution of values of Label in test_labels
test_labels.value_counts(normalize = True)

 0    0.490566
-1    0.283019
 1    0.226415
Name: Label, dtype: float64

# Check the 10 rows of train_data
train_data.head(10)

# Check the 10 rows of valid_data
valid_data.head(10)

# Check the 10 rows of test_data
test_data.head(10)

# Print the first 10 rows of train_labels
train_labels.head(10)

140    0
83    -1
6      1
294    0
90    -1
81     0
13     0
18    -1
124    1
297    0
Name: Label, dtype: int64

train_labels_encoded = le.fit_transform(train_labels)

for i in range(0, 10):
    print(train_labels_encoded[i])

1
0
2
1
0
1
1
0
2
1

# Print the first 10 rows of valid_labels
valid_labels.head(10)

43     0
213    0
323    1
242    0
96     1
32    -1
51     1
78    -1
77     0
291    0
Name: Label, dtype: int64

valid_labels_encoded = le.fit_transform(valid_labels)

for i in range(0, 10):
    print(valid_labels_encoded[i])

1
1
2
1
2
0
2
0
1
1

# Print the first 10 rows of test_labels
test_labels.head(10)

104    0
5      0
21     1
159    0
315   -1
89     0
306   -1
206    0
19     0
52     1
Name: Label, dtype: int64

test_labels_encoded = le.fit_transform(test_labels)

for i in range(0, 10):
    print(test_labels_encoded[i])

1
1
2
1
0
1
0
1
1
2

# Create a list of all words for the column News_Cleaned
words_list = [item.split(" ") for item in stock_news["News_Cleaned"]]

# Create an instance of Word2Vec
vec_size = 300
model_w2v = Word2Vec(words_list, vector_size = vec_size, min_count = 1, window=5, workers = 6)

# Check the size of the above created vocabulary
display(Markdown("**Observations:**\n- Total words in the vocabulary: {}".format(len(list(model_w2v.wv.key_to_index)))))

# Check the word embedding for a random word
model_w2v.wv["stock"]

array([ 3.17709055e-03,  7.47681223e-03, -2.05450575e-03,  5.34986611e-03,
       -5.13954146e-04, -1.15580792e-02,  8.47364683e-03,  1.84548795e-02,
        3.26001109e-03, -2.37264973e-03,  4.25100588e-05, -9.63470526e-03,
        1.22699456e-03,  2.91477633e-03, -9.71277431e-03, -8.12793896e-03,
        5.76221850e-03,  6.38066616e-04,  4.60211374e-03, -4.15469427e-03,
       -6.61033997e-03, -1.25255948e-03,  1.01798922e-02,  2.38442654e-03,
        1.10940253e-02,  1.35063811e-03, -1.17300758e-02, -2.92247278e-03,
       -6.06337609e-03, -8.82460363e-03,  5.54150855e-03, -7.61125484e-05,
        4.07864194e-04,  6.14099146e-04, -5.39152278e-03,  5.02401823e-03,
        9.54323332e-04, -1.38683384e-02, -1.17787451e-03, -2.88353045e-03,
       -1.61134219e-03, -1.58828136e-03,  2.55876128e-03, -8.14945996e-03,
        5.39609231e-03,  1.35154109e-02,  1.18538097e-03, -4.97526082e-04,
       -4.27541975e-03,  6.91649178e-03,  1.30647758e-03, -2.33234162e-03,
       -9.03292838e-03,  3.93311353e-03, -4.78322618e-03,  8.65689758e-03,
        4.49417112e-03, -3.12107941e-03, -1.85551343e-03, -3.83923040e-03,
       -3.49921617e-03, -4.66678664e-03,  6.69020577e-04,  5.00904676e-03,
        7.43207871e-04,  6.41375221e-03, -4.33673104e-03, -2.18218120e-04,
       -4.84429812e-03, -4.34352551e-03, -4.02227184e-03,  6.44994574e-03,
        9.54326242e-03, -7.40756001e-03,  1.13887200e-03,  3.28581198e-03,
       -7.55746989e-03,  2.79041217e-03, -3.33933113e-03,  1.15944045e-02,
       -1.53626571e-03, -1.36262057e-02,  3.40193743e-04,  1.63017679e-02,
        1.93323311e-03, -1.46926974e-03, -3.50160175e-03,  6.61021622e-04,
        7.01755146e-03,  2.63352995e-03,  9.55155492e-03, -6.53040549e-03,
        5.26701845e-03,  3.48650175e-03,  7.28185149e-03,  9.49780643e-03,
        1.19549902e-02, -3.78525374e-03, -7.14790914e-03,  8.38536862e-03,
        4.11777364e-06, -1.75352034e-03,  1.12776142e-02,  4.69591422e-03,
        6.57062687e-04, -7.57858064e-03, -9.20159568e-04,  1.49629603e-03,
       -9.86161456e-03,  1.32339820e-03, -1.10275643e-02, -5.91513189e-03,
        8.67184426e-04,  7.53714982e-03,  6.15932792e-03,  3.58808530e-03,
       -1.99137488e-03, -3.76608525e-03,  8.76014773e-03, -1.42610809e-02,
        4.87278448e-03,  7.93775637e-03,  4.89101838e-03, -4.47228830e-03,
       -6.66818302e-03,  6.79293368e-03,  3.06623429e-03, -5.70640620e-03,
       -1.43174373e-03,  9.38665681e-03,  1.19055039e-05,  1.34311961e-02,
        7.94790278e-04, -9.64298192e-03,  4.61472990e-03,  3.84839857e-03,
       -3.48944752e-03, -6.04470680e-03, -1.28150070e-02, -1.03569562e-02,
        5.77172916e-03, -1.01953717e-02, -8.06121912e-04,  7.24104047e-03,
        8.22413526e-03, -5.50161069e-03, -1.60326287e-02, -5.61936852e-03,
        6.34530606e-03, -3.72481812e-03, -1.24315685e-03, -1.46696037e-02,
       -4.56793513e-03, -7.07024336e-03, -4.22977784e-04,  7.01870676e-03,
       -1.31938774e-02, -2.50009052e-03, -8.17843422e-04,  1.14174075e-02,
        5.43061225e-03,  6.07690960e-03, -1.42692253e-02,  1.37894657e-02,
       -7.17624696e-03,  4.03890479e-03, -2.36295629e-03,  8.21543741e-04,
        3.86367610e-04,  1.41279632e-02, -7.98154972e-04,  1.23873015e-03,
        6.24619471e-03,  8.67904047e-04, -4.07358352e-03,  8.62392597e-04,
        2.25515687e-03, -1.06823090e-02, -9.77365882e-04, -3.18605383e-03,
       -6.17765589e-03,  5.69838006e-03, -9.72298160e-03, -3.14844330e-03,
       -4.92244028e-04,  1.10803265e-03,  1.21233203e-02,  1.48987183e-02,
        9.39873885e-03, -7.41673913e-03, -1.03207037e-03, -2.38261698e-03,
       -1.49164246e-02,  1.57908781e-03,  2.73158797e-03, -6.94685569e-03,
        9.39387246e-04, -1.03107421e-02,  7.78408721e-03, -2.74324464e-03,
       -1.18782585e-02,  7.53213046e-03, -2.08789948e-03, -2.97697238e-03,
        1.21080980e-03, -8.22993461e-04,  1.20201136e-03, -2.76007480e-03,
       -2.35411781e-03, -1.30992674e-03,  3.62621783e-03, -7.85495341e-03,
        1.88483406e-04, -5.75564336e-03,  8.03943258e-03, -8.75872094e-03,
       -6.94269780e-03, -1.80232618e-02, -1.50176128e-02, -1.00830970e-02,
        7.57591706e-03,  4.74905968e-03, -1.72931026e-03, -5.67802507e-03,
       -4.33607399e-03, -6.50667353e-03, -5.21871320e-04, -3.66290845e-03,
       -9.12384968e-03,  1.06572534e-03,  9.69910901e-03, -2.41357530e-03,
       -4.60047461e-03,  1.10975448e-02, -4.92258603e-03,  5.33590000e-03,
       -1.41646585e-03,  4.36059432e-03,  4.55051195e-03, -1.77014172e-02,
        1.02011114e-03, -3.56631447e-03, -8.24630447e-03,  2.53193779e-04,
       -9.87644307e-04, -1.06619727e-02,  1.58009832e-04,  5.23778191e-03,
       -2.07126117e-03,  7.03200512e-03,  1.10228721e-03,  4.58371313e-03,
        4.46452247e-03, -1.74129137e-03, -1.04943067e-02, -5.33928396e-03,
        1.28355296e-02,  4.69208090e-03, -1.25044975e-02, -9.23744403e-03,
        3.89365945e-03,  5.63849788e-03,  2.50953063e-03, -1.32839903e-02,
       -1.05714947e-02,  1.85160944e-03,  1.49266480e-03,  6.18483266e-03,
       -1.10897589e-02,  2.91719893e-03, -9.20408126e-03,  2.22060218e-04,
        2.04212047e-04, -3.50034726e-03,  1.03344778e-02,  7.26595114e-04,
        5.25490521e-03, -7.50531210e-04, -1.04902023e-02, -2.30541546e-03,
        5.84295159e-03, -3.41607188e-03,  1.25261035e-03,  7.02229934e-03,
        2.02644314e-03, -4.35489434e-04, -1.29002109e-02,  3.25774844e-03,
        2.55373842e-03,  1.20471055e-02, -2.29682680e-03,  1.34511087e-02,
        1.07402494e-02,  3.73589632e-04,  1.16554806e-02,  1.37175033e-02,
        2.07187794e-03, -3.54135036e-03,  9.16111469e-03, -1.79410749e-03],
      dtype=float32)

# Check 5 similar words that resemble stock
model_w2v.wv.similar_by_word("stock", topn = 5)

[('appl', 0.9426149129867554),
 ('trade', 0.9330816268920898),
 ('compani', 0.9290030002593994),
 ('year', 0.925611674785614),
 ('servic', 0.9229860901832581)]

# Check the word embedding for a random word
model_w2v.wv["good"]

array([-3.0486875e-03,  6.5574350e-05,  7.4759935e-04,  2.3907982e-03,
        5.0870911e-04, -3.6818287e-03, -1.3009496e-03,  5.9770211e-03,
        1.2470917e-03, -1.0761073e-03,  2.5134687e-03, -3.8001977e-03,
        1.7221153e-04, -2.9597161e-03, -3.1205241e-03, -3.8182659e-03,
       -2.8550574e-03, -1.3705728e-03,  2.0024027e-03, -2.7457606e-03,
       -4.3055434e-03, -2.8421972e-03,  2.2221038e-03, -2.9052366e-03,
        1.3101866e-03, -1.8956354e-04, -2.7342185e-03, -1.5256443e-03,
       -2.8658162e-03, -5.9740216e-04,  3.7740322e-03,  1.8463756e-03,
       -9.8539283e-04,  7.7129633e-04, -3.6049467e-03,  2.9491801e-03,
        9.9868211e-04, -4.5354739e-03,  1.4480204e-05, -2.6394504e-03,
       -3.5278976e-03, -2.9816939e-03, -1.7544647e-03, -3.2574267e-03,
        1.8530585e-03, -1.0577423e-03, -2.6555087e-03, -7.4141583e-04,
       -2.2910282e-03,  8.9874357e-04,  4.4646145e-05,  1.2018952e-03,
        2.0302041e-03,  3.3680685e-03, -1.9739156e-03, -7.7820197e-04,
        2.8914823e-03,  1.7148021e-03,  1.7234661e-04,  1.8493280e-04,
        1.0291828e-03, -2.5828152e-03,  2.5051394e-03,  3.6820048e-03,
        3.0209270e-04, -2.2287189e-03, -1.8899884e-03,  9.8983801e-05,
       -2.0996581e-03,  2.8196531e-03, -2.9492206e-03,  3.6538811e-03,
        9.5302926e-04,  1.0151986e-03,  2.2347777e-03,  1.5737128e-03,
       -1.2691534e-03,  3.1624606e-03, -3.8041797e-04,  1.8073829e-03,
       -3.1576932e-03, -4.5639955e-04, -1.7446299e-03,  5.0377171e-03,
       -1.0209343e-03, -2.5798685e-03, -2.6931777e-03,  3.6592733e-03,
       -5.3639384e-04,  1.1805131e-03,  1.8796449e-03, -3.0142646e-03,
       -6.6230458e-04,  1.0405300e-03,  2.4149169e-05,  2.8016879e-03,
       -7.7565957e-04,  6.9788366e-04, -3.6377262e-03, -5.4965919e-04,
       -9.5060037e-04, -2.9128124e-03,  3.2064512e-03,  2.2864600e-03,
       -8.8843511e-04, -9.7880687e-04,  1.6460939e-03,  3.7536148e-03,
       -2.6034149e-03, -1.0542490e-03, -1.6096537e-03, -4.6714579e-04,
       -4.3378709e-04, -7.4490311e-04,  3.0195806e-03, -9.2334452e-04,
       -1.7374587e-03,  5.9557846e-04, -1.0540643e-03, -1.3486741e-03,
        2.1498741e-03,  2.4586671e-03,  1.1021853e-03,  2.6663139e-03,
        2.3084548e-03,  2.2437931e-03,  1.7410764e-03, -4.1134707e-03,
        2.1491004e-03, -1.0725164e-03,  4.9711554e-05,  2.5402440e-03,
        2.0513299e-03, -8.1689999e-04, -1.9567208e-03, -1.8878556e-03,
        1.1059275e-03, -3.2102577e-03, -1.3798218e-04,  1.0261125e-03,
        1.6223467e-03, -9.2283665e-04, -3.4190305e-03,  4.6838145e-04,
       -1.6273673e-03,  4.1768517e-04, -2.0444603e-03, -8.6621242e-04,
        3.3177363e-03, -1.4800554e-03,  3.1088335e-03, -4.2453697e-03,
       -4.4135726e-03, -1.0940752e-04, -1.3427562e-03, -1.5640893e-03,
       -2.2622372e-03,  2.3728018e-03, -1.8303151e-03,  4.1601267e-03,
       -2.9099619e-04,  2.5284574e-03,  9.7470306e-04,  3.4755790e-03,
       -3.9380131e-04, -7.2984962e-04, -2.3445287e-03, -2.8281133e-03,
        2.1221458e-03, -1.1372617e-04,  2.1944507e-03, -1.3544534e-03,
        3.9177816e-03,  1.2898408e-03, -6.1412319e-04,  3.1418305e-05,
       -4.3236415e-04, -2.7622767e-03, -2.0961137e-03, -8.3184527e-04,
        1.1063467e-03, -1.6310323e-03, -3.2798827e-03,  1.6164354e-03,
        2.2334489e-03, -2.2285534e-03,  4.8726760e-03, -1.5078648e-03,
        5.6361593e-04, -1.1079657e-03, -2.0804964e-03,  8.4503053e-04,
        8.8267878e-04,  7.1749109e-04, -2.4049273e-03, -3.9968770e-03,
        1.4240443e-03, -5.7411258e-04, -1.6429385e-03, -1.8539195e-03,
       -6.4470287e-04,  1.1470972e-03,  2.5178876e-03, -3.6860749e-03,
       -2.1086005e-03, -2.3363062e-04,  2.0931740e-03, -3.0377621e-04,
        4.9284025e-04,  9.0958789e-04,  3.5471574e-03, -4.0459838e-03,
        7.2965043e-04, -1.0464694e-05, -2.1745642e-03, -1.2297375e-03,
       -2.7691943e-03,  1.9016489e-04, -6.1925320e-04, -6.7035615e-04,
       -1.5007664e-03,  2.6669067e-03,  2.2700180e-03,  1.0399196e-03,
        1.5530367e-04, -1.2283257e-03, -6.3582661e-04,  1.6856081e-03,
       -1.7998941e-03,  2.9392994e-03,  7.8749569e-04, -1.2851235e-03,
        1.9541078e-03,  1.2622903e-03, -3.1955906e-03,  3.2643904e-03,
        2.5830234e-03,  2.1380312e-03,  2.7290562e-03, -2.3151231e-03,
       -1.5903611e-03,  1.8386415e-04, -1.0841044e-03,  2.4208867e-03,
       -1.5187516e-03, -4.4400644e-04, -1.8139889e-03,  1.6194425e-03,
       -2.3494249e-03, -1.4290120e-03,  1.3133120e-03,  1.6690003e-03,
       -1.5279839e-03,  2.4510720e-03, -4.4791489e-03,  2.0734379e-03,
        1.2824289e-03, -1.4532551e-04, -3.3989295e-03, -3.7370790e-03,
        1.9517442e-03,  9.1177213e-04,  8.4857235e-04, -8.2082843e-04,
        1.5628176e-03, -6.2325050e-04, -1.6819185e-03, -6.9804933e-05,
       -3.8669887e-03, -1.7748547e-03, -3.1790815e-04, -1.0504506e-03,
        2.7607272e-03,  9.7047660e-04,  1.5082657e-03, -9.5582649e-04,
       -2.1167863e-03,  1.0538180e-03, -4.2809201e-03,  5.1289517e-04,
       -1.8545962e-04,  1.1928250e-03,  6.1696896e-04,  1.4575935e-04,
       -4.9105636e-04, -3.0578854e-03, -1.5595591e-03, -2.3511478e-03,
       -1.5553017e-03,  3.0253907e-03, -1.8009744e-03,  4.9401266e-03,
        2.9135153e-03,  1.3890995e-03, -1.6572084e-03,  5.0043617e-03,
        5.5485440e-04, -3.4647798e-03,  3.4946455e-03, -3.0458067e-04],
      dtype=float32)

# Check 5 similar words that resemble good
model_w2v.wv.similar_by_word("good", topn = 5)

[('industri', 0.46960991621017456),
 ('stock', 0.4595384895801544),
 ('billion', 0.45387163758277893),
 ('appl', 0.4523754417896271),
 ('rate', 0.45218122005462646)]

# Check the word embedding for a random word
model_w2v.wv["best"]

array([-3.0331462e-04, -5.3468614e-04, -2.3507965e-03,  2.3238910e-03,
        6.2924613e-05,  2.2667905e-03, -1.9202539e-03, -5.4290867e-04,
       -1.3062355e-05,  3.2631017e-04,  3.7091775e-04,  3.1755771e-04,
       -7.8727398e-04, -3.2323361e-03,  2.3102986e-03,  2.4261645e-03,
       -9.9606765e-04, -2.6650857e-03,  1.9631335e-03, -2.6238288e-03,
        1.2216629e-03,  2.3698502e-03, -5.3439173e-04, -1.5041154e-03,
       -2.2064268e-03, -1.8839987e-03, -7.6569116e-04, -2.7578834e-03,
        2.7155194e-03, -2.1656787e-03, -1.6140644e-03, -1.7673238e-03,
        2.5934097e-04, -1.0655877e-03,  1.2869083e-03, -1.5524542e-03,
       -1.7018823e-03,  1.0515834e-03,  1.7982097e-03,  1.2592894e-03,
        1.7052518e-03, -9.6276519e-04,  3.1057408e-03, -2.8921294e-03,
        1.8760421e-03, -1.1938106e-04, -6.8213104e-04, -2.0304413e-03,
       -1.8525523e-03,  1.3693069e-03, -4.4781107e-05, -2.3467334e-03,
        7.8856165e-04, -2.9503750e-03,  2.9562874e-04,  1.5092996e-03,
       -2.2503261e-03, -3.2267405e-03, -2.0918879e-03,  3.7792552e-04,
        2.5633567e-03,  5.5331842e-04, -4.5649646e-04,  5.4560689e-04,
        3.2676037e-03, -2.1483817e-03, -4.6570789e-05,  2.4681829e-03,
        4.0219264e-05, -2.6334384e-03,  1.2969493e-04, -8.1513008e-06,
       -1.7742459e-03, -3.0352140e-03,  1.3117223e-03,  2.6399128e-03,
        2.1038190e-03,  2.2796265e-04,  2.9489796e-03, -1.4907836e-03,
       -5.0589541e-04, -2.4980227e-03,  1.6571837e-03,  2.5581550e-03,
        4.2378387e-04, -8.5884013e-04, -3.1497537e-03, -1.0511992e-03,
        2.6550491e-03, -1.5013204e-03,  3.7995086e-03, -2.8346933e-03,
        2.1879030e-03,  3.3388659e-03, -1.3152205e-03, -1.9609807e-03,
        1.5679906e-03, -3.1107564e-03,  3.9725922e-04,  1.0097454e-03,
       -1.3981056e-03, -4.6248686e-05,  7.5933029e-04, -2.7247269e-03,
        1.6671838e-03,  2.5694771e-03, -1.5827123e-03, -1.4566791e-03,
       -2.6318799e-03,  8.2409935e-04,  2.0037428e-03,  1.1774955e-03,
        1.8530207e-03,  1.1352998e-04, -1.7005628e-03,  1.9693177e-03,
        1.3910631e-03,  2.9964659e-03, -1.2020378e-03, -3.1669366e-03,
       -4.4215974e-04, -9.2357746e-04, -9.2889671e-04,  3.3295499e-05,
       -2.8510380e-04, -2.5160180e-03, -7.2229348e-05, -4.9268600e-04,
       -3.8485069e-04, -7.2574272e-04,  7.2141318e-04,  4.0300186e-03,
       -3.0119780e-03,  1.7543075e-04,  2.2947975e-03, -9.0561871e-04,
        5.1803992e-04,  2.1438643e-03, -2.9940018e-03,  6.2587368e-04,
       -7.5157284e-04, -9.4640377e-04, -1.7373257e-03, -1.4476264e-03,
       -2.2778616e-03, -1.5116477e-03,  1.6334236e-03,  3.6162179e-04,
        2.5346372e-03, -4.3537388e-05,  1.3057519e-03,  2.1536925e-06,
       -4.5041670e-04,  8.5507566e-04,  2.8960800e-03,  2.5951264e-03,
       -3.1794636e-03, -4.2976410e-04, -8.2421623e-04,  2.1596046e-03,
        1.7793993e-03,  3.1378625e-03, -1.3135857e-03, -1.5822931e-03,
        1.8208415e-03,  3.2224681e-03,  1.9130892e-03, -1.7503598e-04,
        9.2705665e-04, -1.5116355e-03,  2.9949395e-03, -7.9569680e-04,
       -2.3177227e-03,  1.1748000e-03, -9.1128965e-04, -7.2822650e-04,
       -2.0336967e-03, -3.4125787e-03, -2.0070523e-03, -4.1639723e-04,
       -1.4406740e-03,  3.0889807e-03, -2.7422892e-04,  1.0708302e-03,
       -3.0382970e-04,  2.3390253e-03, -2.4633768e-03,  2.2052606e-03,
       -2.0988521e-04,  1.1146647e-03,  1.2950131e-04,  1.8542435e-03,
        1.7320975e-03,  5.0830079e-04, -2.8819260e-03,  6.3209579e-04,
        1.9276083e-03,  1.4526369e-04, -2.2063940e-03, -2.5613194e-03,
        1.1021817e-03, -2.7124344e-03,  2.6677726e-04,  2.0719497e-03,
        3.2428795e-04, -2.4526478e-03,  9.1887219e-04, -2.3905989e-03,
        2.6605835e-03,  5.6684145e-04, -4.2161270e-04, -1.5569255e-03,
       -1.8107907e-04, -1.9872566e-03,  6.9839480e-05,  3.3814259e-04,
       -1.5590464e-03, -1.8263090e-03, -2.5611576e-03,  1.8872765e-03,
        3.5162026e-03,  2.5113428e-03, -2.9874083e-03, -2.7529662e-03,
       -3.6924768e-03, -3.2463397e-03,  3.0077242e-03,  2.9406019e-03,
       -7.1595516e-04, -2.5800331e-03,  3.0178237e-03, -1.2413925e-03,
        1.2620277e-03,  1.3739644e-03,  1.4267825e-04,  4.4496730e-04,
        4.2973025e-04,  5.4917188e-04,  1.9369128e-03,  1.4714235e-03,
        5.8986992e-04, -1.6040244e-03,  3.1589731e-04, -1.5783039e-03,
       -3.1099693e-04, -3.9226389e-03,  1.5790267e-04,  2.9292128e-03,
       -1.1027589e-03,  3.3230106e-03,  1.3089129e-03,  2.4504890e-03,
        1.9777433e-03, -1.9188268e-03, -2.2790418e-03, -3.1241388e-03,
        1.0803088e-03, -9.7461388e-04, -6.4783485e-04,  2.7619849e-03,
       -1.6357854e-03, -1.0462881e-03, -3.1366660e-03,  4.7737453e-04,
        1.3858662e-03,  2.5049003e-03, -1.1538747e-03, -3.0506891e-04,
        1.3949901e-03,  2.4074104e-04, -3.5013233e-03,  8.5182715e-04,
       -2.1728296e-03, -2.9231084e-03,  4.1759508e-03, -1.9447637e-03,
        1.8048830e-03,  7.1944558e-04, -1.1485514e-03, -9.4725681e-04,
        2.5340570e-03, -9.3347748e-04, -2.4197579e-03, -1.1043171e-03,
        1.3543292e-03,  2.4474727e-03,  8.3541573e-04, -1.6294974e-03,
        1.8840489e-03, -2.3236496e-03, -3.0674590e-03,  3.3886433e-03,
       -1.2986810e-03, -5.5846147e-04, -2.5803356e-03,  3.7409929e-03,
        1.6715587e-03, -1.0979024e-03, -1.2259193e-03,  2.0156917e-03],
      dtype=float32)

# Check 5 similar words that resemble good
model_w2v.wv.similar_by_word("best", topn = 5)

[('came', 0.21230290830135345),
 ('time', 0.20437480509281158),
 ('chip', 0.20015497505664825),
 ('stream', 0.197169229388237),
 ('reveal', 0.19689200818538666)]

# Retrieve words present in the vocabulary of model_w2v
words = list(model_w2v.wv.key_to_index.keys())

# Retrieve word vectors for all the words present in the vocabulary of model_w2v
words_vectors = model_w2v.wv[words].tolist()

# Create a dictionary of words and their corresponding vectors
word_vector_dict = dict(zip(words, words_vectors))

# Display the length of the dictionary word_vector_dict
display(Markdown("**Observations:**\n- Length of the dictionary is {}.".format(len(word_vector_dict))))

# Use the function average_vectorizer_Word2Vec and vecotise the vectors for train_data
train_data_word_vectors = pd.DataFrame(train_data["News_Cleaned"].apply(average_vectorizer_Word2Vec).tolist(), columns = ["Feature " + str(i) for i in range(vec_size)])

# Use the function average_vectorizer_Word2Vec and vecotise the vectors for valid_data
valid_data_word_vectors = pd.DataFrame(valid_data["News_Cleaned"].apply(average_vectorizer_Word2Vec).tolist(), columns = ["Feature " + str(i) for i in range(vec_size)])

# Use the function average_vectorizer_Word2Vec and vecotise the vectors for test_data
test_data_word_vectors = pd.DataFrame(test_data["News_Cleaned"].apply(average_vectorizer_Word2Vec).tolist(), columns = ["Feature " + str(i) for i in range(vec_size)])

# Display the 10 rows from train_data_word_vectors
train_data_word_vectors.head(10)

# Display the 10 rows from valid_data_word_vectors
valid_data_word_vectors.head(10)

# Display the 10 rows from test_data_word_vectors
test_data_word_vectors.head(10)

# Display the size of the dataset
display(Markdown("**Observations:**\n- Train Data Shape: {}\n- Validation Data Shape: {}\n- Test Data Shape: {}".format(train_data_word_vectors.shape, valid_data_word_vectors.shape, test_data_word_vectors.shape)))

# Use the glve_file to initialize GloVe model
glove_model = KeyedVectors.load_word2vec_format(glove_file, binary = False)

# Display the length of the vocabulary of the glove_model
display(Markdown("**Observations:**\n- Total words in the vocabulary: {}".format(len(glove_model.index_to_key))))

# Check the embedding for a random word
glove_model["stock"]

array([ 8.6341e-01,  6.9648e-01,  4.5794e-02, -9.5708e-03, -2.5498e-01,
       -7.4666e-01, -2.2086e-01, -4.4615e-01, -1.0423e-01, -9.9931e-01,
        7.2550e-02,  4.5049e-01, -5.9912e-02, -5.7837e-01, -4.6540e-01,
        4.3429e-02, -5.0570e-01, -1.5442e-01,  9.8250e-01, -8.1571e-02,
        2.6523e-01, -2.3734e-01,  9.7675e-02,  5.8588e-01, -1.2948e-01,
       -6.8956e-01, -1.2811e-01, -5.2265e-02, -6.7719e-01,  3.0190e-02,
        1.8058e-01,  8.6121e-01, -8.3206e-01, -5.6887e-02, -2.9578e-01,
        4.7180e-01,  1.2811e+00, -2.5228e-01,  4.9557e-02, -7.2455e-01,
        6.6758e-01, -1.1091e+00, -2.0493e-01, -5.8669e-01, -2.5375e-03,
        8.2777e-01, -4.9102e-01, -2.6475e-01,  4.3015e-01, -2.0516e+00,
       -3.3208e-01,  5.1845e-02,  5.2646e-01,  8.7452e-01, -9.0237e-01,
       -1.7366e+00, -3.4727e-01,  1.6590e-01,  2.7727e+00,  6.5756e-02,
       -4.0363e-01,  3.8252e-01, -3.0787e-01,  5.9202e-01,  1.3468e-01,
       -3.3851e-01,  3.3646e-01,  2.0950e-01,  8.5905e-01,  5.1865e-01,
       -1.0657e+00, -2.6371e-02, -3.1349e-01,  2.3231e-01, -7.0192e-01,
       -5.5737e-01, -2.3418e-01,  1.3563e-01, -1.0016e+00, -1.4221e-01,
        1.0372e+00,  3.5880e-01, -4.2608e-01, -1.9386e-01, -3.7867e-01,
       -6.9646e-01, -3.9989e-01, -5.7782e-01,  1.0132e-01,  2.0123e-01,
       -3.7153e-01,  5.0837e-01, -3.7758e-01, -2.6205e-01, -9.3676e-01,
        1.0053e+00,  8.4393e-01, -2.4698e-01,  1.7339e-01,  9.4473e-01],
      dtype=float32)

# Check the similar word for the random word
glove_model.most_similar("stock", topn = 5)

[('shares', 0.8525474667549133),
 ('stocks', 0.8309942483901978),
 ('market', 0.7991610765457153),
 ('exchange', 0.784952700138092),
 ('trading', 0.7632875442504883)]

# Check the embedding for a random word
glove_model["good"]

array([-0.030769 ,  0.11993  ,  0.53909  , -0.43696  , -0.73937  ,
       -0.15345  ,  0.081126 , -0.38559  , -0.68797  , -0.41632  ,
       -0.13183  , -0.24922  ,  0.441    ,  0.085919 ,  0.20871  ,
       -0.063582 ,  0.062228 , -0.051234 , -0.13398  ,  1.1418   ,
        0.036526 ,  0.49029  , -0.24567  , -0.412    ,  0.12349  ,
        0.41336  , -0.48397  , -0.54243  , -0.27787  , -0.26015  ,
       -0.38485  ,  0.78656  ,  0.1023   , -0.20712  ,  0.40751  ,
        0.32026  , -0.51052  ,  0.48362  , -0.0099498, -0.38685  ,
        0.034975 , -0.167    ,  0.4237   , -0.54164  , -0.30323  ,
       -0.36983  ,  0.082836 , -0.52538  , -0.064531 , -1.398    ,
       -0.14873  , -0.35327  , -0.1118   ,  1.0912   ,  0.095864 ,
       -2.8129   ,  0.45238  ,  0.46213  ,  1.6012   , -0.20837  ,
       -0.27377  ,  0.71197  , -1.0754   , -0.046974 ,  0.67479  ,
       -0.065839 ,  0.75824  ,  0.39405  ,  0.15507  , -0.64719  ,
        0.32796  , -0.031748 ,  0.52899  , -0.43886  ,  0.67405  ,
        0.42136  , -0.11981  , -0.21777  , -0.29756  , -0.1351   ,
        0.59898  ,  0.46529  , -0.58258  , -0.02323  , -1.5442   ,
        0.01901  , -0.015877 ,  0.024499 , -0.58017  , -0.67659  ,
       -0.040379 , -0.44043  ,  0.083292 ,  0.20035  , -0.75499  ,
        0.16918  , -0.26573  , -0.52878  ,  0.17584  ,  1.065    ],
      dtype=float32)

# Check the similar word for the random word
glove_model.most_similar("good", topn = 5)

[('better', 0.893191397190094),
 ('sure', 0.8314563035964966),
 ('really', 0.8297762274742126),
 ('kind', 0.8288268446922302),
 ('very', 0.8260800242424011)]

# List the words in the glove_model
glove_words = glove_model.index_to_key

# format the vectors in a dictionary
glove_word_vector_dict = dict(zip(glove_model.index_to_key,list(glove_model.vectors)))

# Set the vector size to 100
vec_size = 100

# Use the function average_vectorizer_GloVe() to vectorize the train_data
train_data_glove = pd.DataFrame(train_data["News_Cleaned"].apply(average_vectorizer_GloVe).tolist(), columns = ["Feature " + str(i) for i in range(vec_size)])

# Use the function average_vectorizer_GloVe() to vectorize the valid_data
valid_data_glove = pd.DataFrame(valid_data["News_Cleaned"].apply(average_vectorizer_GloVe).tolist(), columns = ["Feature " + str(i) for i in range(vec_size)])

# Use the function average_vectorizer_GloVe() to vectorize the test_data
test_data_glove = pd.DataFrame(test_data["News_Cleaned"].apply(average_vectorizer_GloVe).tolist(), columns = ["Feature " + str(i) for i in range(vec_size)])

# Display the 10 rows from train_data_glove
train_data_glove.head(10)

# Display the 10 rows from valid_data_glove
valid_data_glove.head(10)

# Display the 10 rows from test_data_glove
test_data_glove.head(10)

# Display the size of the dataset
display(Markdown("**Observations:**\n- Train Data Shape: {}\n- Validation Data Shape: {}\n- Test Data Shape: {}".format(train_data_glove.shape, valid_data_glove.shape, test_data_glove.shape)))

# defining the Sentence Transformer model
sentence_transformer_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# Test the model for a few test values
sentence_transformer_model.encode(["The tech sector experienced a significant decline in the aftermarket following Apple's Q1 revenue warning. Notable suppliers, including Skyworks, Broadcom, Lumentum, Qorvo, and TSMC, saw their stocks drop in response to Apple's downward revision of its revenue expectations for the quarter, previously announced in January."])

array([[-2.02315999e-03, -3.67735103e-02,  7.73542672e-02,
         4.67134863e-02,  3.25521231e-02,  2.10231100e-03,
         4.32834513e-02,  3.95345651e-02,  5.82279973e-02,
         8.87510274e-03,  7.09637105e-02,  4.99076620e-02,
         6.46608397e-02, -4.97965980e-03, -1.30518768e-02,
        -2.98356246e-02, -8.91321059e-03, -7.82000348e-02,
        -2.17109472e-02, -5.24822846e-02, -5.14276214e-02,
        -3.30719762e-02, -3.32051516e-02,  4.18125428e-02,
         7.99547806e-02,  1.54092433e-02, -2.15781312e-02,
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        -1.03700548e-01, -6.20274385e-03,  3.49569470e-02,
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         3.95032391e-02,  1.34979258e-03, -7.48429224e-02,
         5.03437445e-02,  5.74472873e-03, -2.39702333e-02,
         1.32124184e-03,  6.57341406e-02, -5.62655106e-02,
        -6.76572174e-02, -4.22493778e-02, -2.64710654e-02,
         2.83033252e-02,  4.93411794e-02,  3.14059146e-02,
        -3.22604701e-02, -1.48834558e-02, -7.07753524e-02,
        -9.63546149e-03, -3.38620655e-02, -1.05421124e-02,
        -5.47044203e-02,  2.66298149e-02,  5.15929498e-02,
        -1.93036944e-02,  1.74268465e-02, -2.24287640e-02,
         5.11573453e-04,  2.79130228e-02, -6.66390359e-02,
        -5.92727400e-02,  3.25676589e-03, -5.93541306e-04,
        -5.50960526e-02, -2.71337014e-02, -3.57516576e-03,
        -1.31502077e-01,  7.41634443e-02,  5.75100817e-02]], dtype=float32)

# Check the cosine similarity score for the first two values

# Get the first value in sentence1
sentence1 = "The tech sector experienced a significant decline in the aftermarket following Apple's Q1 revenue warning. Notable suppliers, including Skyworks, Broadcom, Lumentum, Qorvo, and TSMC, saw their stocks drop in response to Apple's downward revision of its revenue expectations for the quarter, previously announced in January."

# Get the second value in sentence2
sentence2 = "Apple lowered its fiscal Q1 revenue guidance to $84 billion from earlier estimates of $89-$93 billion due to weaker than expected iPhone sales. The announcement caused a significant drop in Apple's stock price and negatively impacted related suppliers, leading to broader market declines for tech indices such as Nasdaq 10"

# Display the cosine similarity with the function cosine_score
display(Markdown("**Observations:**\n- Cosine Similarity Score: {}".format(cosine_score(sentence_transformer_model, [sentence1, sentence2]))))

# Setting the device to GPU if available, else CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Display the devide being used
display(Markdown("**Observations:**\n- Using device: {}".format(device)))

# Use sentence_transformer_model.encode() to transform the News to encode in train_data
train_data_transformer = sentence_transformer_model.encode(train_data["News"].values, device = device, show_progress_bar = True)

# Use sentence_transformer_model.encode() to transform the News to encode in valid_data
valid_data_transformer = sentence_transformer_model.encode(valid_data["News"].values, device = device, show_progress_bar = True)

# Use sentence_transformer_model.encode() to transform the News to encode in test_data
test_data_transformer = sentence_transformer_model.encode(test_data["News"].values, device = device, show_progress_bar = True)

# Display the size of the dataset
display(Markdown("**Observations:**\n- Train Data Shape: {}\n- Validation Data Shape: {}\n- Test Data Shape: {}".format(train_data_transformer.shape, valid_data_transformer.shape, test_data_transformer.shape)))

# Conver the dataset into dataframe with 384 columns for train_data_transformer
train_data_transformer = pd.DataFrame(train_data_transformer, columns = ["Feature " + str(i) for i in range(0, 384)])

# Conver the dataset into dataframe with 384 columns for valid_data_transformer
valid_data_transformer = pd.DataFrame(valid_data_transformer, columns = ["Feature " + str(i) for i in range(0, 384)])

# Conver the dataset into dataframe with 384 columns for test_data_transformer
test_data_transformer = pd.DataFrame(test_data_transformer, columns = ["Feature " + str(i) for i in range(0, 384)])

# Print the 10 rows of the train_data_transformer
train_data_transformer.head(10)

# Print the 10 rows of the valid_data_transformer
valid_data_transformer.head(10)

# Print the 10 rows of the test_data_transformer
test_data_transformer.head(10)

# Define an DecisionTreeClassifier model
wv_base_dtc_model = DecisionTreeClassifier(random_state = 42)

# Fit the model on the train_data_word_vectors
wv_base_dtc_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(wv_base_dtc_model, train_data_word_vectors, train_labels, "wv_base_dtc_model_report_train")

# Calculate different metrics on train_data_word_vectors
wv_base_dtc_model_report_train = model_performance_classification_report(wv_base_dtc_model, train_data_word_vectors, train_labels, "wv_base_dtc_model_report_train")

# Print the dataframe wv_base_dtc_model_report_train
print("Training performance:\n", wv_base_dtc_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                           Model
0       1.0     1.0        1.0  1.0  wv_base_dtc_model_report_train

# Use the model on valid_data_word_vectors
confusion_matrix(wv_base_dtc_model, valid_data_word_vectors, valid_labels, "wv_base_dtc_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
wv_base_dtc_model_report_valid = model_performance_classification_report(wv_base_dtc_model, valid_data_word_vectors, valid_labels, "wv_base_dtc_model_report_valid")

# Print the dataframe wv_base_dtc_model_report_valid
print("Validation performance:\n", wv_base_dtc_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                           Model
0  0.423077  0.423077   0.407925  0.411658  wv_base_dtc_model_report_valid

# Define an RandomForestClassifier model
wv_base_rf_model = RandomForestClassifier(random_state = 42)

# Fit the model on the train_data_word_vectors
wv_base_rf_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(wv_base_rf_model, train_data_word_vectors, train_labels, "wv_base_rf_model_report_train")

# Calculate different metrics on train_data_word_vectors
wv_base_rf_model_report_train = model_performance_classification_report(wv_base_rf_model, train_data_word_vectors, train_labels, "wv_base_rf_model_report_train")

# Print the dataframe wv_base_rf_model_report_train
print("Training performance:\n", wv_base_rf_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                          Model
0       1.0     1.0        1.0  1.0  wv_base_rf_model_report_train

# Use the model on valid_data_word_vectors
confusion_matrix(wv_base_rf_model, valid_data_word_vectors, valid_labels, "wv_base_rf_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
wv_base_rf_model_report_valid = model_performance_classification_report(wv_base_rf_model, valid_data_word_vectors, valid_labels, "wv_base_rf_model_report_valid")

# Print the dataframe wv_base_rf_model_report_valid
print("Validation performance:\n", wv_base_rf_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision       F1                          Model
0  0.423077  0.423077   0.310651  0.35285  wv_base_rf_model_report_valid

# Define an AdaboostClassifier model
wv_base_ab_model = AdaBoostClassifier(random_state = 42)

# Fit the model on the train_data_word_vectors
wv_base_ab_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(wv_base_ab_model, train_data_word_vectors, train_labels, "wv_base_ab_model_report_train")

# Calculate different metrics on train_data_word_vectors
wv_base_ab_model_report_train = model_performance_classification_report(wv_base_ab_model, train_data_word_vectors, train_labels, "wv_base_ab_model_report_train")

# Print the dataframe wv_base_ab_model_report_train
print("Training performance:\n", wv_base_ab_model_report_train)

Training performance:
    Accuracy    Recall  Precision       F1                          Model
0  0.790984  0.790984    0.80099  0.78992  wv_base_ab_model_report_train

# Use the model on valid_data_word_vectors
confusion_matrix(wv_base_ab_model, valid_data_word_vectors, valid_labels, "wv_base_ab_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
wv_base_ab_model_report_valid = model_performance_classification_report(wv_base_ab_model, valid_data_word_vectors, valid_labels, "wv_base_ab_model_report_valid")

# Print the dataframe wv_base_ab_model_report_valid
print("Validation performance:\n", wv_base_ab_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                          Model
0  0.423077  0.423077   0.356937  0.360662  wv_base_ab_model_report_valid

# Define an GradientBoostingClassifier model
wv_base_gb_model = GradientBoostingClassifier(random_state = 42)

# Fit the model on the train_data_word_vectors
wv_base_gb_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(wv_base_gb_model, train_data_word_vectors, train_labels, "wv_base_gb_model_report_train")

# Calculate different metrics on train_data_word_vectors
wv_base_gb_model_report_train = model_performance_classification_report(wv_base_gb_model, train_data_word_vectors, train_labels, "wv_base_gb_model_report_train")

# Print the dataframe wv_base_gb_model_report_train
print("Training performance:\n", wv_base_gb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                          Model
0       1.0     1.0        1.0  1.0  wv_base_gb_model_report_train

# Use the model on valid_data_word_vectors
confusion_matrix(wv_base_gb_model, valid_data_word_vectors, valid_labels, "wv_base_gb_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
wv_base_gb_model_report_valid = model_performance_classification_report(wv_base_gb_model, valid_data_word_vectors, valid_labels, "wv_base_gb_model_report_valid")

# Print the dataframe wv_base_gb_model_report_valid
print("Validation performance:\n", wv_base_gb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                          Model
0  0.384615  0.384615     0.3713  0.357311  wv_base_gb_model_report_valid

# Define an XGBClassifier model
wv_base_xgb_model = XGBClassifier(random_state = 42)

# Fit the model on the train_data_word_vectors
wv_base_xgb_model.fit(train_data_word_vectors, train_labels_encoded)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(wv_base_xgb_model, train_data_word_vectors, train_labels_encoded, "wv_base_xgb_model_report_train")

# Calculate different metrics on train_data_word_vectors
wv_base_xgb_model_report_train = model_performance_classification_report(wv_base_xgb_model, train_data_word_vectors, train_labels_encoded, "wv_base_xgb_model_report_train")

# Print the dataframe wv_base_xgb_model_report_train
print("Training performance:\n", wv_base_xgb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                           Model
0       1.0     1.0        1.0  1.0  wv_base_xgb_model_report_train

# Use the model on valid_data_word_vectors
confusion_matrix(wv_base_xgb_model, valid_data_word_vectors, valid_labels_encoded, "wv_base_xgb_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
wv_base_xgb_model_report_valid = model_performance_classification_report(wv_base_xgb_model, valid_data_word_vectors, valid_labels_encoded, "wv_base_xgb_model_report_valid")

# Print the dataframe wv_base_xgb_model_report_valid
print("Validation performance:\n", wv_base_xgb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                           Model
0  0.423077  0.423077   0.421105  0.385216  wv_base_xgb_model_report_valid

# Define an DecisionTreeClassifier model
glove_base_dtc_model = DecisionTreeClassifier(random_state = 42)

# Fit the model on the train_data_glove
glove_base_dtc_model.fit(train_data_glove, train_labels)

# Plot the confusion matrix for the train_data_glove
confusion_matrix(glove_base_dtc_model, train_data_glove, train_labels, "glove_base_dtc_model_report_train")

# Calculate different metrics on train_data_glove
glove_base_dtc_model_report_train = model_performance_classification_report(glove_base_dtc_model, train_data_glove, train_labels, "glove_base_dtc_model_report_train")

# Print the dataframe glove_base_dtc_model_report_train
print("Training performance:\n", glove_base_dtc_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                              Model
0       1.0     1.0        1.0  1.0  glove_base_dtc_model_report_train

# Use the model on valid_data_glove
confusion_matrix(glove_base_dtc_model, valid_data_glove, valid_labels, "glove_base_dtc_model_report_valid")

# Calculate different metrics on valid_data_glove
glove_base_dtc_model_report_valid = model_performance_classification_report(glove_base_dtc_model, valid_data_glove, valid_labels, "glove_base_dtc_model_report_valid")

# Print the dataframe glove_base_dtc_model_report_valid
print("Validation performance:\n", glove_base_dtc_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                              Model
0  0.480769  0.480769   0.488905  0.480008  glove_base_dtc_model_report_valid

# Define an AdaBoostClassifier model
glove_base_rf_model = RandomForestClassifier(random_state = 42)

# Fit the model on the train_data_glove
glove_base_rf_model.fit(train_data_glove, train_labels)

# Plot the confusion matrix for the train_data_glove
confusion_matrix(glove_base_rf_model, train_data_glove, train_labels, "glove_base_rf_model_report_train")

# Calculate different metrics on train_data_glove
glove_base_rf_model_report_train = model_performance_classification_report(glove_base_rf_model, train_data_glove, train_labels, "glove_base_rf_model_report_train")

# Print the dataframe glove_base_rf_model_report_train
print("Training performance:\n", glove_base_rf_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                             Model
0       1.0     1.0        1.0  1.0  glove_base_rf_model_report_train

# Use the model on valid_data_glove
confusion_matrix(glove_base_rf_model, valid_data_glove, valid_labels, "glove_base_rf_model_report_valid")

# Calculate different metrics on valid_data_glove
glove_base_rf_model_report_valid = model_performance_classification_report(glove_base_rf_model, valid_data_glove, valid_labels, "glove_base_rf_model_report_valid")

# Print the dataframe glove_base_rf_model_report_valid
print("Validation performance:\n", glove_base_rf_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                             Model
0  0.519231  0.519231   0.621248  0.456832  glove_base_rf_model_report_valid

# Define an AdaBoostClassifier model
glove_base_ab_model = AdaBoostClassifier(random_state = 42)

# Fit the model on the train_data_glove
glove_base_ab_model.fit(train_data_glove, train_labels)

# Plot the confusion matrix for the train_data_glove
confusion_matrix(glove_base_ab_model, train_data_glove, train_labels, "glove_base_ab_model_report_train")

# Calculate different metrics on train_data_glove
glove_base_ab_model_report_train = model_performance_classification_report(glove_base_ab_model, train_data_glove, train_labels, "glove_base_ab_model_report_train")

# Print the dataframe glove_base_ab_model_report_train
print("Training performance:\n", glove_base_ab_model_report_train)

Training performance:
    Accuracy    Recall  Precision        F1                             Model
0  0.848361  0.848361   0.849216  0.848412  glove_base_ab_model_report_train

# Use the model on valid_data_glove
confusion_matrix(glove_base_ab_model, valid_data_glove, valid_labels, "glove_base_ab_model_report_valid")

# Calculate different metrics on valid_data_glove
glove_base_ab_model_report_valid = model_performance_classification_report(glove_base_ab_model, valid_data_glove, valid_labels, "glove_base_ab_model_report_valid")

# Print the dataframe glove_base_ab_model_report_valid
print("Validation performance:\n", glove_base_ab_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                             Model
0  0.365385  0.365385   0.382784  0.371118  glove_base_ab_model_report_valid

# Define an GradientBoosting model
glove_base_gb_model = GradientBoostingClassifier(random_state = 42)

# Fit the model on the train_data_glove
glove_base_gb_model.fit(train_data_glove, train_labels)

# Plot the confusion matrix for the train_data_glove
confusion_matrix(glove_base_gb_model, train_data_glove, train_labels, "glove_base_gb_model_report_train")

# Calculate different metrics on train_data_glove
glove_base_gb_model_report_train = model_performance_classification_report(glove_base_gb_model, train_data_glove, train_labels, "glove_base_gb_model_report_train")

# Print the dataframe glove_base_gb_model_report_train
print("Training performance:\n", glove_base_gb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                             Model
0       1.0     1.0        1.0  1.0  glove_base_gb_model_report_train

# Use the model on valid_data_glove
confusion_matrix(glove_base_gb_model, valid_data_glove, valid_labels, "glove_base_gb_model_report_valid")

# Calculate different metrics on valid_data_glove
glove_base_gb_model_report_valid = model_performance_classification_report(glove_base_gb_model, valid_data_glove, valid_labels, "glove_base_gb_model_report_valid")

# Print the dataframe glove_base_gb_model_report_valid
print("Validation performance:\n", glove_base_gb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                             Model
0  0.365385  0.365385   0.337865  0.349288  glove_base_gb_model_report_valid

# Define an XGBClassifier model
glove_base_xgb_model = XGBClassifier(random_state = 42)

# Fit the model on the train_data_glove
glove_base_xgb_model.fit(train_data_glove, train_labels_encoded)

# Plot the confusion matrix for the train_data_glove
confusion_matrix(glove_base_xgb_model, train_data_glove, train_labels_encoded, "glove_base_xgb_model_report_train")

# Calculate different metrics on train_data_glove
glove_base_xgb_model_report_train = model_performance_classification_report(glove_base_xgb_model, train_data_glove, train_labels_encoded, "glove_base_xgb_model_report_train")

# Print the dataframe glove_base_xgb_model_report_train
print("Training performance:\n", glove_base_xgb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                              Model
0       1.0     1.0        1.0  1.0  glove_base_xgb_model_report_train

# Use the model on valid_data_glove
confusion_matrix(glove_base_xgb_model, valid_data_glove, valid_labels_encoded, "glove_base_xgb_model_report_valid")

# Calculate different metrics on valid_data_glove
glove_base_xgb_model_report_valid = model_performance_classification_report(glove_base_xgb_model, valid_data_glove, valid_labels_encoded, "glove_base_xgb_model_report_valid")

# Print the dataframe glove_base_xgb_model_report_valid
print("Validation performance:\n", glove_base_xgb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                              Model
0  0.442308  0.442308   0.382692  0.389252  glove_base_xgb_model_report_valid

# Define an DecisionTreeClassifier model
transformer_base_dtc_model = DecisionTreeClassifier(random_state = 42)

# Fit the model on the train_data_transformer
transformer_base_dtc_model.fit(train_data_transformer, train_labels)

# Plot the confusion matrix for the train_data_transformer
confusion_matrix(transformer_base_dtc_model, train_data_transformer, train_labels, "transformer_base_dtc_model_report_train")

# Calculate different metrics on train_data_transformer
transformer_base_dtc_model_report_train = model_performance_classification_report(transformer_base_dtc_model, train_data_transformer, train_labels, "transformer_base_dtc_model_report_train")

# Print the dataframe transformer_base_dtc_model_report_train
print("Training performance:\n", transformer_base_dtc_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                                    Model
0       1.0     1.0        1.0  1.0  transformer_base_dtc_model_report_train

# Use the model on valid_data_transformer
confusion_matrix(transformer_base_dtc_model, valid_data_transformer, valid_labels, "transformer_base_dtc_model_report_valid")

# Calculate different metrics on valid_data_transformer
transformer_base_dtc_model_report_valid = model_performance_classification_report(transformer_base_dtc_model, valid_data_transformer, valid_labels, "transformer_base_dtc_model_report_valid")

# Print the dataframe transformer_base_dtc_model_report_valid
print("Validation performance:\n", transformer_base_dtc_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1  \
0  0.403846  0.403846   0.391765  0.394776   

                                     Model  
0  transformer_base_dtc_model_report_valid

# Define an AdaBoostClassifier model
transformer_base_rf_model = RandomForestClassifier(random_state = 42)

# Fit the model on the train_data_transformer
transformer_base_rf_model.fit(train_data_transformer, train_labels)

# Plot the confusion matrix for the train_data_transformer
confusion_matrix(transformer_base_rf_model, train_data_transformer, train_labels, "transformer_base_rf_model_report_train")

# Calculate different metrics on train_data_transformer
transformer_base_rf_model_report_train = model_performance_classification_report(transformer_base_rf_model, train_data_transformer, train_labels, "transformer_base_rf_model_report_train")

# Print the dataframe sentence_transformer_base_rf_model_report_train
print("Training performance:\n", transformer_base_rf_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                                   Model
0       1.0     1.0        1.0  1.0  transformer_base_rf_model_report_train

# Use the model on valid_data_transformer
confusion_matrix(transformer_base_rf_model, valid_data_transformer, valid_labels, "transformer_base_rf_model_report_valid")

# Calculate different metrics on valid_data_transformer
transformer_base_rf_model_report_valid = model_performance_classification_report(transformer_base_rf_model, valid_data_transformer, valid_labels, "transformer_base_rf_model_report_valid")

# Print the dataframe transformer_base_rf_model_report_valid
print("Validation performance:\n", transformer_base_rf_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1  \
0  0.403846  0.403846   0.335623  0.329668   

                                    Model  
0  transformer_base_rf_model_report_valid

# Define an AdaBoostClassifier model
transformer_base_ab_model = AdaBoostClassifier(random_state = 42)

# Fit the model on the train_data_transformer
transformer_base_ab_model.fit(train_data_transformer, train_labels)

# Plot the confusion matrix for the train_data_transformer
confusion_matrix(transformer_base_ab_model, train_data_transformer, train_labels, "transformer_base_ab_model_report_train")

# Calculate different metrics on train_data_transformer
transformer_base_ab_model_report_train = model_performance_classification_report(transformer_base_ab_model, train_data_transformer, train_labels, "transformer_base_ab_model_report_train")

# Print the dataframe sentence_transformer_base_ab_model_report_train
print("Training performance:\n", transformer_base_ab_model_report_train)

Training performance:
    Accuracy   Recall  Precision       F1  \
0   0.79918  0.79918   0.805554  0.79919   

                                    Model  
0  transformer_base_ab_model_report_train

# Use the model on valid_data_transformer
confusion_matrix(transformer_base_ab_model, valid_data_transformer, valid_labels, "transformer_base_ab_model_report_valid")

# Calculate different metrics on valid_data_transformer
transformer_base_ab_model_report_valid = model_performance_classification_report(transformer_base_ab_model, valid_data_transformer, valid_labels, "transformer_base_ab_model_report_valid")

# Print the dataframe transformer_base_ab_model_report_valid
print("Validation performance:\n", transformer_base_ab_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1  \
0  0.519231  0.519231   0.505845  0.495774   

                                    Model  
0  transformer_base_ab_model_report_valid

# Define an GradientBoostingClassifier model
transformer_base_gb_model = GradientBoostingClassifier(random_state = 42)

# Fit the model on the train_data_transformer
transformer_base_gb_model.fit(train_data_transformer, train_labels)

# Plot the confusion matrix for the train_data_transformer
confusion_matrix(transformer_base_gb_model, train_data_transformer, train_labels, "transformer_base_gb_model_report_train")

# Calculate different metrics on train_data_transformer
transformer_base_gb_model_report_train = model_performance_classification_report(transformer_base_gb_model, train_data_transformer, train_labels, "transformer_base_gb_model_report_train")

# Print the dataframe transformer_base_gb_model_report_train
print("Training performance:\n", transformer_base_gb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                                   Model
0       1.0     1.0        1.0  1.0  transformer_base_gb_model_report_train

# Use the model on valid_data_transformer
confusion_matrix(transformer_base_gb_model, valid_data_transformer, valid_labels, "transformer_base_gb_model_report_valid")

# Calculate different metrics on valid_data_transformer
transformer_base_gb_model_report_valid = model_performance_classification_report(transformer_base_gb_model, valid_data_transformer, valid_labels, "transformer_base_gb_model_report_valid")

# Print the dataframe transformer_base_gb_model_report_valid
print("Validation performance:\n", transformer_base_gb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1  \
0  0.403846  0.403846   0.394591  0.384258   

                                    Model  
0  transformer_base_gb_model_report_valid

# Define an XGBoostClassifier model
transformer_base_xgb_model = XGBClassifier(random_state = 42)

# Fit the model on the train_data_transformer
transformer_base_xgb_model.fit(train_data_transformer, train_labels_encoded)

# Plot the confusion matrix for the train_data_transformer
confusion_matrix(transformer_base_xgb_model, train_data_transformer, train_labels_encoded, "transformer_base_xgb_model_report_train")

# Calculate different metrics on train_data_transformer
transformer_base_xgb_model_report_train = model_performance_classification_report(transformer_base_xgb_model, train_data_transformer, train_labels_encoded, "transformer_base_xgb_model_report_train")

# Print the dataframe transformer_base_xgb_model_report_train
print("Training performance:\n", transformer_base_xgb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                                    Model
0       1.0     1.0        1.0  1.0  transformer_base_xgb_model_report_train

# Use the model on valid_data_transformer
confusion_matrix(transformer_base_xgb_model, valid_data_transformer, valid_labels, "transformer_base_xgb_model_report_valid")

# Calculate different metrics on valid_data_transformer
transformer_base_xgb_model_report_valid = model_performance_classification_report(transformer_base_xgb_model, valid_data_transformer, valid_labels_encoded, "transformer_base_xgb_model_report_valid")

# Print the dataframe transformer_base_xgb_model_report_valid
print("Validation performance:\n", transformer_base_xgb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1  \
0  0.442308  0.442308   0.415335  0.412475   

                                     Model  
0  transformer_base_xgb_model_report_valid

# Define the parameter_grid
parameter_grid = {
    # Function to measure quality of a split
    "criterion": ["gini", "entropy"],  

    # Depth of the tree (None allows full depth)
    "max_depth": [5, 10, 15, 20, None],  

    # Minimum samples needed to split a node
    "min_samples_split": [2, 5, 10],  

    # Minimum samples required at a leaf node
    "min_samples_leaf": [1, 2, 5, 10],  

    # Number of features to consider for best split
    "max_features": ["sqrt", "log2", None]  
}

# Initiate a XGBClassifier
wv_tune_dtc_grid = DecisionTreeClassifier(random_state = 42)

# Initialize the grid_search
wv_tune_dtc_grid_search = GridSearchCV(estimator = wv_tune_dtc_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
wv_tune_dtc_grid_result = wv_tune_dtc_grid_search.fit(train_data_word_vectors, train_labels)

# Print the best parameters
print("Best parameters:", wv_tune_dtc_grid_search.best_params_)

Fitting 5 folds for each of 360 candidates, totalling 1800 fits

huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)

Best parameters: {'criterion': 'gini', 'max_depth': 15, 'max_features': 'log2', 'min_samples_leaf': 2, 'min_samples_split': 2}

# Set the classification to the best combination of parameters
best_params = wv_tune_dtc_grid_search.best_params_

# Create the tuned model
wv_tune_dtc_model = DecisionTreeClassifier(**best_params, random_state = 42)

# Fit the best algorithm to the data.
wv_tune_dtc_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(wv_tune_dtc_model, train_data_word_vectors, train_labels, "wv_tune_dtc_model_report_train")

# Calculate different metrics on train_data_word_vectors
wv_tune_dtc_model_report_train = model_performance_classification_report(wv_tune_dtc_model, train_data_word_vectors, train_labels, "wv_tune_dtc_model_report_train")

# Print the dataframe wv_tune_dtc_model_report_train
print("Training performance:\n", wv_tune_dtc_model_report_train)

Training performance:
    Accuracy    Recall  Precision        F1                           Model
0  0.909836  0.909836    0.92438  0.910022  wv_tune_dtc_model_report_train

# Plot the confusion matrix for the valid_data_word_vectors
confusion_matrix(wv_tune_dtc_model, valid_data_word_vectors, valid_labels, "wv_tune_dtc_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
wv_tune_dtc_model_report_valid = model_performance_classification_report(wv_tune_dtc_model, valid_data_word_vectors, valid_labels, "wv_tune_dtc_model_report_valid")

# Print the dataframe wv_tune_dtc_model_report_valid
print("Validation performance:\n", wv_tune_dtc_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                           Model
0  0.365385  0.365385   0.341963  0.351474  wv_tune_dtc_model_report_valid

# Define the parameter_grid
parameter_grid = {
    
    "n_estimators": [100, 200, 300],
    "max_depth": [10, 20, 30, None],
    "criterion": ["gini", "entropy"]
}

# Initiate a AdaBoostClassifier
wv_tune_rf_grid = RandomForestClassifier(random_state = 42)

# Initialize the grid_search
wv_tune_rf_grid_search = GridSearchCV(estimator = wv_tune_rf_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
wv_tune_rf_grid_result = wv_tune_rf_grid_search.fit(train_data_word_vectors, train_labels)

# Print the best parameters
print("Best parameters:", wv_tune_rf_grid_result.best_params_)

Fitting 5 folds for each of 24 candidates, totalling 120 fits
Best parameters: {'criterion': 'gini', 'max_depth': 10, 'n_estimators': 100}

# Set the classification to the best combination of parameters
best_params = wv_tune_rf_grid_search.best_params_

# Create the tuned model
wv_tune_rf_model = RandomForestClassifier(**best_params, random_state = 42)

# Fit the best algorithm to the data.
wv_tune_rf_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(wv_tune_rf_model, train_data_word_vectors, train_labels, "wv_tune_rf_model_report_train")

# Calculate different metrics on train_data_word_vectors
wv_tune_rf_model_report_train = model_performance_classification_report(wv_tune_rf_model, train_data_word_vectors, train_labels, "wv_tune_rf_model_report_train")

# Print the dataframe wv_tune_rf_model_report_train
print("Training performance:\n", wv_tune_rf_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                          Model
0       1.0     1.0        1.0  1.0  wv_tune_rf_model_report_train

# Plot the confusion matrix for the valid_data_word_vectors
confusion_matrix(wv_tune_rf_model, valid_data_word_vectors, valid_labels, "wv_tune_rf_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
wv_tune_rf_model_report_valid = model_performance_classification_report(wv_tune_rf_model, valid_data_word_vectors, valid_labels, "wv_tune_rf_model_report_valid")

# Print the dataframe wv_tune_rf_model_report_valid
print("Validation performance:\n", wv_tune_rf_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                          Model
0  0.480769  0.480769   0.466828  0.422205  wv_tune_rf_model_report_valid

# Define the parameter_grid
parameter_grid = {

  # Number of boosting rounds
  "n_estimators": [50, 100, 200],

  # Step size shrinkage
  "learning_rate": [0.01, 0.1, 1.0]
}

# Initiate a AdaBoostClassifier
wv_tune_ada_grid = AdaBoostClassifier(random_state = 42)

# Initialize the grid_search
wv_tune_ab_grid_search = GridSearchCV(estimator = wv_tune_ada_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
wv_tune_ab_grid_result = wv_tune_ab_grid_search.fit(train_data_word_vectors, train_labels)

# Print the best parameters
print("Best parameters:", wv_tune_ab_grid_search.best_params_)

Fitting 5 folds for each of 9 candidates, totalling 45 fits

/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(

Best parameters: {'learning_rate': 0.1, 'n_estimators': 200}

# Set the classification to the best combination of parameters
best_params = wv_tune_ab_grid_search.best_params_

# Create the tuned model
wv_tune_ab_model = AdaBoostClassifier(**best_params, random_state = 42)

# Fit the best algorithm to the data.
wv_tune_ab_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(wv_tune_ab_model, train_data_word_vectors, train_labels, "wv_tune_ab_model_report_train")

# Calculate different metrics on train_data_word_vectors
wv_tune_ab_model_report_train = model_performance_classification_report(wv_tune_ab_model, train_data_word_vectors, train_labels, "wv_tune_ab_model_report_train")

# Print the dataframe wv_base_ab_model_report_train
print("Training performance:\n", wv_tune_ab_model_report_train)

Training performance:
    Accuracy    Recall  Precision        F1                          Model
0  0.758197  0.758197   0.800528  0.750878  wv_tune_ab_model_report_train

# Plot the confusion matrix for the valid_data_word_vectors
confusion_matrix(wv_tune_ab_model, valid_data_word_vectors, valid_labels, "wv_tune_ab_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
wv_tune_ab_model_report_valid = model_performance_classification_report(wv_tune_ab_model, valid_data_word_vectors, valid_labels, "wv_tune_ab_model_report_valid")

# Print the dataframe wv_tune_ab_model_report_valid
print("Validation performance:\n", wv_tune_ab_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                          Model
0  0.519231  0.519231   0.505601  0.479741  wv_tune_ab_model_report_valid

# Define the parameter_grid
parameter_grid = {
    
    # Number of boosting rounds
    "n_estimators": [100, 200, 300],

    # Shrinkage parameter
    "learning_rate": [0.01, 0.1, 0.2]
}

# Initiate a XGBClassifier
wv_tune_gb_grid = GradientBoostingClassifier(random_state = 42)

# Initialize the grid_search
wv_tune_gb_grid_search = GridSearchCV(estimator = wv_tune_gb_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
wv_tune_gb_grid_result = wv_tune_gb_grid_search.fit(train_data_word_vectors, train_labels)

# Print the best parameters
print("Best parameters:", wv_tune_gb_grid_search.best_params_)

Fitting 5 folds for each of 9 candidates, totalling 45 fits
Best parameters: {'learning_rate': 0.2, 'n_estimators': 100}

# Set the classification to the best combination of parameters
best_params = wv_tune_gb_grid_search.best_params_

# Create the tuned model
wv_tune_gb_model = GradientBoostingClassifier(**best_params, random_state = 42)

# Fit the best algorithm to the data.
wv_tune_gb_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(wv_tune_gb_model, train_data_word_vectors, train_labels, "wv_tune_gb_model_report_train")

# Calculate different metrics on train_data_word_vectors
wv_tune_gb_model_report_train = model_performance_classification_report(wv_tune_gb_model, train_data_word_vectors, train_labels, "wv_tune_gb_model_report_train")

# Print the dataframe wv_tune_xgb_model_report_train
print("Training performance:\n", wv_tune_gb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                          Model
0       1.0     1.0        1.0  1.0  wv_tune_gb_model_report_train

# Plot the confusion matrix for the valid_data_word_vectors
confusion_matrix(wv_tune_gb_model, valid_data_word_vectors, valid_labels, "wv_tune_gb_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
wv_tune_gb_model_report_valid = model_performance_classification_report(wv_tune_gb_model, valid_data_word_vectors, valid_labels, "wv_tune_gb_model_report_valid")

# Print the dataframe wv_tune_gb_model_report_valid
print("Validation performance:\n", wv_tune_gb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                          Model
0  0.346154  0.346154   0.315934  0.319096  wv_tune_gb_model_report_valid

# Define the parameter_grid
parameter_grid = {

  # Number of boosting rounds
  "n_estimators": [50, 100, 200],

  # Step size shrinkage
  "learning_rate": [0.01, 0.1, 0.3],

  # Define max_depth
  "max_depth": [3, 5, 7],

  # Define subsample
  "subsample": [0.6, 0.8, 1.0]
}

# Initiate a XGBClassifier
wv_tune_xgb_grid = XGBClassifier(random_state = 42)

# Initialize the grid_search
wv_tune_xgb_grid_search = GridSearchCV(estimator = wv_tune_xgb_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
wv_tune_xbg_grid_result = wv_tune_xgb_grid_search.fit(train_data_word_vectors, train_labels_encoded)

# Print the best parameters
print("Best parameters:", wv_tune_xgb_grid_search.best_params_)

Fitting 5 folds for each of 81 candidates, totalling 405 fits
Best parameters: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100, 'subsample': 0.8}

# Set the classification to the best combination of parameters
best_params = wv_tune_xgb_grid_search.best_params_

# Create the tuned model
wv_tune_xgb_model = XGBClassifier(**best_params, random_state = 42)

# Fit the best algorithm to the data.
wv_tune_xgb_model.fit(train_data_word_vectors, train_labels_encoded)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(wv_tune_xgb_model, train_data_word_vectors, train_labels_encoded, "wv_tune_xgb_model_report_train")

# Calculate different metrics on train_data_word_vectors
wv_tune_xgb_model_report_train = model_performance_classification_report(wv_tune_xgb_model, train_data_word_vectors, train_labels_encoded, "wv_tune_xgb_model_report_train")

# Print the dataframe wv_tune_xgb_model_report_train
print("Training performance:\n", wv_tune_xgb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                           Model
0       1.0     1.0        1.0  1.0  wv_tune_xgb_model_report_train

# Plot the confusion matrix for the valid_data_word_vectors
confusion_matrix(wv_tune_xgb_model, valid_data_word_vectors, valid_labels_encoded, "wv_tune_xgb_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
wv_tune_xgb_model_report_valid = model_performance_classification_report(wv_tune_xgb_model, valid_data_word_vectors, valid_labels_encoded, "wv_tune_xgb_model_report_valid")

# Print the dataframe wv_tune_xgb_model_report_valid
print("Validation performance:\n", wv_tune_xgb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                           Model
0  0.461538  0.461538   0.491124  0.424279  wv_tune_xgb_model_report_valid

# Define the parameter_grid
parameter_grid = {
    # Function to measure quality of a split
    "criterion": ["gini", "entropy"],  

    # Depth of the tree (None allows full depth)
    "max_depth": [5, 10, 15, 20, None],  

    # Minimum samples needed to split a node
    "min_samples_split": [2, 5, 10],  

    # Minimum samples required at a leaf node
    "min_samples_leaf": [1, 2, 5, 10],  

    # Number of features to consider for best split
    "max_features": ["sqrt", "log2", None]  
}

# Initiate a XGBClassifier
glove_tune_dtc_grid = DecisionTreeClassifier(random_state = 42)

# Initialize the grid_search
glove_tune_dtc_grid_search = GridSearchCV(estimator = glove_tune_dtc_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
glove_tune_dtc_grid_result = glove_tune_dtc_grid_search.fit(train_data_word_vectors, train_labels)

# Print the best parameters
print("Best parameters:", glove_tune_dtc_grid_search.best_params_)

Fitting 5 folds for each of 360 candidates, totalling 1800 fits
Best parameters: {'criterion': 'gini', 'max_depth': 15, 'max_features': 'log2', 'min_samples_leaf': 2, 'min_samples_split': 2}

# Set the classification to the best combination of parameters
best_params = glove_tune_dtc_grid_search.best_params_

# Create the tuned model
glove_tune_dtc_model = DecisionTreeClassifier(**best_params, random_state = 42)

# Fit the best algorithm to the data.
glove_tune_dtc_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(glove_tune_dtc_model, train_data_word_vectors, train_labels, "glove_tune_dtc_model_report_train")

# Calculate different metrics on train_data_word_vectors
glove_tune_dtc_model_report_train = model_performance_classification_report(glove_tune_dtc_model, train_data_word_vectors, train_labels, "glove_tune_dtc_model_report_train")

# Print the dataframe glove_tune_dtc_model_report_train
print("Training performance:\n", glove_tune_dtc_model_report_train)

Training performance:
    Accuracy    Recall  Precision        F1                              Model
0  0.909836  0.909836    0.92438  0.910022  glove_tune_dtc_model_report_train

# Plot the confusion matrix for the valid_data_word_vectors
confusion_matrix(glove_tune_dtc_model, valid_data_word_vectors, valid_labels, "glove_tune_dtc_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
glove_tune_dtc_model_report_valid = model_performance_classification_report(glove_tune_dtc_model, valid_data_word_vectors, valid_labels, "glove_tune_dtc_model_report_valid")

# Print the dataframe wv_tune_dtc_model_report_valid
print("Validation performance:\n", glove_tune_dtc_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                              Model
0  0.365385  0.365385   0.341963  0.351474  glove_tune_dtc_model_report_valid

# Define the parameter_grid
parameter_grid = {
    "n_estimators": [100, 200, 300],
    "max_depth": [10, 20, 30, None],
    "criterion": ["gini", "entropy"]
}

# Initiate a RandomForestClassifier
glove_tune_rf_grid = RandomForestClassifier(random_state = 42)

# Initialize the grid_search
glove_tune_rf_grid_search = GridSearchCV(estimator = glove_tune_rf_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
glove_tune_rf_grid_result = glove_tune_rf_grid_search.fit(train_data_word_vectors, train_labels)

# Print the best parameters
print("Best parameters:", glove_tune_rf_grid_result.best_params_)

Fitting 5 folds for each of 24 candidates, totalling 120 fits
Best parameters: {'criterion': 'gini', 'max_depth': 10, 'n_estimators': 100}

# Set the classification to the best combination of parameters
best_params = glove_tune_rf_grid_search.best_params_

# Create the tuned model
glove_tune_rf_model = RandomForestClassifier(**best_params, random_state = 42)

# Fit the best algorithm to the data.
glove_tune_rf_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(glove_tune_rf_model, train_data_word_vectors, train_labels, "glove_tune_rf_model_report_train")

# Calculate different metrics on train_data_word_vectors
glove_tune_rf_model_report_train = model_performance_classification_report(glove_tune_rf_model, train_data_word_vectors, train_labels, "glove_tune_rf_model_report_train")

# Print the dataframe glove_tune_rf_model_report_train
print("Training performance:\n", glove_tune_rf_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                             Model
0       1.0     1.0        1.0  1.0  glove_tune_rf_model_report_train

# Plot the confusion matrix for the valid_data_word_vectors
confusion_matrix(glove_tune_rf_model, valid_data_word_vectors, valid_labels, "glove_tune_rf_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
glove_tune_rf_model_report_valid = model_performance_classification_report(glove_tune_rf_model, valid_data_word_vectors, valid_labels, "glove_tune_rf_model_report_valid")

# Print the dataframe glove_tune_rf_model_report_valid
print("Validation performance:\n", glove_tune_rf_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                             Model
0  0.480769  0.480769   0.466828  0.422205  glove_tune_rf_model_report_valid

# Define the parameter_grid
parameter_grid = {

  # Number of boosting rounds
  "n_estimators": [50, 100, 200],

  # Step size shrinkage
  "learning_rate": [0.01, 0.1, 1.0]
}

# Initiate a AdaBoostClassifier
glove_tune_ada_grid = AdaBoostClassifier(random_state = 42)

# Initialize the grid_search
glove_tune_ab_grid_search = GridSearchCV(estimator = glove_tune_ada_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
glove_tune_ab_grid_result = glove_tune_ab_grid_search.fit(train_data_glove, train_labels)

# Print the best parameters
print("Best parameters:", glove_tune_ab_grid_result.best_params_)

Fitting 5 folds for each of 9 candidates, totalling 45 fits

/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(

Best parameters: {'learning_rate': 1.0, 'n_estimators': 50}

# Set the classifier to the best combination of parameters
glove_tune_ab_model = glove_tune_ab_grid_result.best_estimator_

# Fit the best algorithm to the data.
glove_tune_ab_model.fit(train_data_glove, train_labels)

# Plot the confusion matrix for the train_data_glove
confusion_matrix(glove_tune_ab_model, train_data_glove, train_labels, "glove_tune_ab_model_report_train")

# Calculate different metrics on train_data_glove
glove_tune_ab_model_report_train = model_performance_classification_report(glove_tune_ab_model, train_data_glove, train_labels, "glove_tune_ab_model_report_train")

# Print the dataframe glove_tune_ab_model_report_train
print("Training performance:\n", glove_tune_ab_model_report_train)

Training performance:
    Accuracy    Recall  Precision        F1                             Model
0  0.848361  0.848361   0.849216  0.848412  glove_tune_ab_model_report_train

# Plot the confusion matrix for the valid_data_glove
confusion_matrix(glove_tune_ab_model, valid_data_glove, valid_labels, "glove_tune_ab_model_report_valid")

# Calculate different metrics on valid_data_glove
glove_tune_ab_model_report_valid = model_performance_classification_report(glove_tune_ab_model, valid_data_glove, valid_labels, "glove_tune_ab_model_report_valid")

# Print the dataframe glove_tune_ab_model_report_valid
print("Validation performance:\n", glove_tune_ab_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                             Model
0  0.365385  0.365385   0.382784  0.371118  glove_tune_ab_model_report_valid

# Define the parameter_grid
parameter_grid = {
    
    # Number of boosting rounds
    "n_estimators": [100, 200, 300],

    # Shrinkage parameter
    "learning_rate": [0.01, 0.1, 0.2]
}

# Initiate a XGBClassifier
glove_tune_gb_grid = GradientBoostingClassifier(random_state = 42)

# Initialize the grid_search
glove_tune_gb_grid_search = GridSearchCV(estimator = glove_tune_gb_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
glove_tune_gb_grid_result = glove_tune_gb_grid_search.fit(train_data_word_vectors, train_labels)

# Print the best parameters
print("Best parameters:", glove_tune_gb_grid_search.best_params_)

Fitting 5 folds for each of 9 candidates, totalling 45 fits
Best parameters: {'learning_rate': 0.2, 'n_estimators': 100}

# Set the classification to the best combination of parameters
best_params = glove_tune_gb_grid_search.best_params_

# Create the tuned model
glove_tune_gb_model = GradientBoostingClassifier(**best_params, random_state = 42)

# Fit the best algorithm to the data.
glove_tune_gb_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(glove_tune_gb_model, train_data_word_vectors, train_labels, "glove_tune_gb_model_report_train")

# Calculate different metrics on train_data_word_vectors
glove_tune_gb_model_report_train = model_performance_classification_report(glove_tune_gb_model, train_data_word_vectors, train_labels, "glove_tune_gb_model_report_train")

# Print the dataframe wv_tune_xgb_model_report_train
print("Training performance:\n", glove_tune_gb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                             Model
0       1.0     1.0        1.0  1.0  glove_tune_gb_model_report_train

# Plot the confusion matrix for the valid_data_word_vectors
confusion_matrix(glove_tune_gb_model, valid_data_word_vectors, valid_labels, "glove_tune_gb_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
glove_tune_gb_model_report_valid = model_performance_classification_report(glove_tune_gb_model, valid_data_word_vectors, valid_labels, "glove_tune_gb_model_report_valid")

# Print the dataframe glove_tune_gb_model_report_valid
print("Validation performance:\n", glove_tune_gb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                             Model
0  0.346154  0.346154   0.315934  0.319096  glove_tune_gb_model_report_valid

# Define the parameter_grid
parameter_grid = {

  # Number of boosting rounds
  "n_estimators": [50, 100, 200],

  # Step size shrinkage
  "learning_rate": [0.01, 0.1, 0.3],

  # Define max_depth
  "max_depth": [3, 5, 7],

  # Define subsample
  "subsample": [0.6, 0.8, 1.0]
}

# Initiate a XGBClassifier
glove_tune_xgb_grid = XGBClassifier(random_state = 42)

# Initialize the grid_search
glove_tune_xgb_grid_search = GridSearchCV(estimator = glove_tune_xgb_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
glove_tune_xbg_grid_result = glove_tune_xgb_grid_search.fit(train_data_glove, train_labels_encoded)

# Print the best parameters
print("Best parameters:", glove_tune_xgb_grid_search.best_params_)

Fitting 5 folds for each of 81 candidates, totalling 405 fits
Best parameters: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 200, 'subsample': 0.8}

# Set the classifier to the best combination of parameters
glove_tune_xgb_model = glove_tune_xbg_grid_result.best_estimator_

# Fit the best algorithm to the data.
glove_tune_xgb_model.fit(train_data_glove, train_labels_encoded)

# Plot the confusion matrix for the train_data_glove
confusion_matrix(glove_tune_xgb_model, train_data_glove, train_labels_encoded, "glove_tune_xgb_model_report_train")

# Calculate different metrics on train_data_glove
glove_tune_xgb_model_report_train = model_performance_classification_report(glove_tune_xgb_model, train_data_glove, train_labels_encoded, "glove_tune_xgb_model_report_train")

# Print the dataframe glove_tune_xgb_model_report_train
print("Training performance:\n", glove_tune_xgb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                              Model
0       1.0     1.0        1.0  1.0  glove_tune_xgb_model_report_train

# Plot the confusion matrix for the valid_data_glove
confusion_matrix(glove_tune_xgb_model, valid_data_glove, valid_labels_encoded, "glove_tune_xgb_model_report_valid")

# Calculate different metrics on valid_data_glove
glove_tune_xgb_model_report_valid = model_performance_classification_report(glove_tune_xgb_model, valid_data_glove, valid_labels_encoded, "glove_tune_xgb_model_report_valid")

# Print the dataframe glove_tune_xgb_model_report_valid
print("Validation performance:\n", glove_tune_xgb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                              Model
0  0.519231  0.519231   0.519643  0.492773  glove_tune_xgb_model_report_valid

# Define the parameter_grid
parameter_grid = {
    # Function to measure quality of a split
    "criterion": ["gini", "entropy"],  

    # Depth of the tree (None allows full depth)
    "max_depth": [5, 10, 15, 20, None],  

    # Minimum samples needed to split a node
    "min_samples_split": [2, 5, 10],  

    # Minimum samples required at a leaf node
    "min_samples_leaf": [1, 2, 5, 10],  

    # Number of features to consider for best split
    "max_features": ["sqrt", "log2", None]  
}

# Initiate a XGBClassifier
transformer_tune_dtc_grid = DecisionTreeClassifier(random_state = 42)

# Initialize the grid_search
transformer_tune_dtc_grid_search = GridSearchCV(estimator = transformer_tune_dtc_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
transformer_tune_dtc_grid_result = transformer_tune_dtc_grid_search.fit(train_data_word_vectors, train_labels)

# Print the best parameters
print("Best parameters:", transformer_tune_dtc_grid_result.best_params_)

Fitting 5 folds for each of 360 candidates, totalling 1800 fits
Best parameters: {'criterion': 'gini', 'max_depth': 15, 'max_features': 'log2', 'min_samples_leaf': 2, 'min_samples_split': 2}

# Set the classification to the best combination of parameters
best_params = transformer_tune_dtc_grid_search.best_params_

# Create the tuned model
transformer_tune_dtc_model = DecisionTreeClassifier(**best_params, random_state = 42)

# Fit the best algorithm to the data.
transformer_tune_dtc_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(transformer_tune_dtc_model, train_data_word_vectors, train_labels, "transformer_tune_dtc_model_report_train")

# Calculate different metrics on train_data_word_vectors
transformer_tune_dtc_model_report_train = model_performance_classification_report(transformer_tune_dtc_model, train_data_word_vectors, train_labels, "transformer_tune_dtc_model_report_train")

# Print the dataframe transformer_tune_dtc_model_report_train
print("Training performance:\n", transformer_tune_dtc_model_report_train)

Training performance:
    Accuracy    Recall  Precision        F1  \
0  0.909836  0.909836    0.92438  0.910022   

                                     Model  
0  transformer_tune_dtc_model_report_train

# Plot the confusion matrix for the valid_data_word_vectors
confusion_matrix(transformer_tune_dtc_model, valid_data_word_vectors, valid_labels, "transformer_tune_dtc_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
transformer_tune_dtc_model_report_valid = model_performance_classification_report(transformer_tune_dtc_model, valid_data_word_vectors, valid_labels, "glove_tune_dtc_model_report_valid")

# Print the dataframe transformer_tune_dtc_model_report_valid
print("Validation performance:\n", transformer_tune_dtc_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1                              Model
0  0.365385  0.365385   0.341963  0.351474  glove_tune_dtc_model_report_valid

# Define the parameter_grid
parameter_grid = {
    "n_estimators": [100, 200, 300],
    "max_depth": [10, 20, 30, None],
    "criterion": ["gini", "entropy"]
}

# Initiate a RandomForestClassifier
transformer_tune_rf_grid = RandomForestClassifier(random_state = 42)

# Initialize the grid_search
transformer_tune_rf_grid_search = GridSearchCV(estimator = transformer_tune_rf_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
transformer_tune_rf_grid_result = transformer_tune_rf_grid_search.fit(train_data_word_vectors, train_labels)

# Print the best parameters
print("Best parameters:", transformer_tune_rf_grid_result.best_params_)

Fitting 5 folds for each of 24 candidates, totalling 120 fits
Best parameters: {'criterion': 'gini', 'max_depth': 10, 'n_estimators': 100}

# Set the classification to the best combination of parameters
best_params = transformer_tune_rf_grid_search.best_params_

# Create the tuned model
transformer_tune_rf_model = RandomForestClassifier(**best_params, random_state = 42)

# Fit the best algorithm to the data.
transformer_tune_rf_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(transformer_tune_rf_model, train_data_word_vectors, train_labels, "transformer_tune_rf_model_report_train")

# Calculate different metrics on train_data_word_vectors
transformer_tune_rf_model_report_train = model_performance_classification_report(transformer_tune_rf_model, train_data_word_vectors, train_labels, "transformer_tune_rf_model_report_train")

# Print the dataframe transformer_tune_rf_model_report_train
print("Training performance:\n", transformer_tune_rf_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                                   Model
0       1.0     1.0        1.0  1.0  transformer_tune_rf_model_report_train

# Plot the confusion matrix for the valid_data_word_vectors
confusion_matrix(transformer_tune_rf_model, valid_data_word_vectors, valid_labels, "transformer_tune_rf_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
transformer_tune_rf_model_report_valid = model_performance_classification_report(transformer_tune_rf_model, valid_data_word_vectors, valid_labels, "transformer_tune_rf_model_report_valid")

# Print the dataframe transformer_tune_rf_model_report_valid
print("Validation performance:\n", transformer_tune_rf_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1  \
0  0.480769  0.480769   0.466828  0.422205   

                                    Model  
0  transformer_tune_rf_model_report_valid

# Define the parameter_grid
parameter_grid = {

  # Number of boosting rounds
  "n_estimators": [50, 100, 200],

  # Step size shrinkage
  "learning_rate": [0.01, 0.1, 1.0]
}

# Initiate a AdaBoostClassifier
transformer_tune_ada_grid = AdaBoostClassifier(random_state = 42)

# Initialize the grid_search
transformer_tune_ab_grid_search = GridSearchCV(estimator = transformer_tune_ada_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
transformer_tune_ab_grid_result = transformer_tune_ab_grid_search.fit(train_data_transformer, train_labels)

# Print the best parameters
print("Best parameters:", transformer_tune_ab_grid_result.best_params_)

Fitting 5 folds for each of 9 candidates, totalling 45 fits

/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(
/opt/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(

Best parameters: {'learning_rate': 1.0, 'n_estimators': 100}

# Set the classification model to the best combination of parameters
transformer_tune_ab_model = transformer_tune_ab_grid_result.best_estimator_

# Fit the best algorithm to the data.
transformer_tune_ab_model.fit(train_data_transformer, train_labels)

# Plot the confusion matrix for the train_data_transformer
confusion_matrix(transformer_tune_ab_model, train_data_transformer, train_labels, "transformer_tune_ab_model_report_train")

# Calculate different metrics on train_data_transformer
transformer_tune_ab_model_report_train = model_performance_classification_report(transformer_tune_ab_model, train_data_transformer, train_labels, "transformer_tune_ab_model_report_train")

# Print the dataframe transformer_tune_ab_model_report_train
print("Training performance:\n", transformer_tune_ab_model_report_train)

Training performance:
    Accuracy    Recall  Precision        F1  \
0  0.868852  0.868852   0.873107  0.868837   

                                    Model  
0  transformer_tune_ab_model_report_train

# Plot the confusion matrix for the valid_data_transformer
confusion_matrix(transformer_tune_ab_model, valid_data_transformer, valid_labels, "transformer_tune_ab_model_report_valid")

# Calculate different metrics on valid_data_transformer
transformer_tune_ab_model_report_valid = model_performance_classification_report(transformer_tune_ab_model, valid_data_transformer, valid_labels, "transformer_tune_ab_model_report_valid")

# Print the dataframe transformer_tune_ab_model_report_valid
print("Validation performance:\n", transformer_tune_ab_model_report_valid)

Validation performance:
    Accuracy  Recall  Precision        F1  \
0       0.5     0.5   0.483087  0.455114   

                                    Model  
0  transformer_tune_ab_model_report_valid

# Define the parameter_grid
parameter_grid = {
    
    # Number of boosting rounds
    "n_estimators": [100, 200, 300],

    # Shrinkage parameter
    "learning_rate": [0.01, 0.1, 0.2]
}

# Initiate a XGBClassifier
transformer_tune_gb_grid = GradientBoostingClassifier(random_state = 42)

# Initialize the grid_search
transformer_tune_gb_grid_search = GridSearchCV(estimator = transformer_tune_gb_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
transformer_tune_gb_grid_result = transformer_tune_gb_grid_search.fit(train_data_word_vectors, train_labels)

# Print the best parameters
print("Best parameters:", transformer_tune_gb_grid_search.best_params_)

Fitting 5 folds for each of 9 candidates, totalling 45 fits
Best parameters: {'learning_rate': 0.2, 'n_estimators': 100}

# Set the classification to the best combination of parameters
best_params = transformer_tune_gb_grid_search.best_params_

# Create the tuned model
transformer_tune_gb_model = GradientBoostingClassifier(**best_params, random_state = 42)

# Fit the best algorithm to the data.
transformer_tune_gb_model.fit(train_data_word_vectors, train_labels)

# Plot the confusion matrix for the train_data_word_vectors
confusion_matrix(transformer_tune_gb_model, train_data_word_vectors, train_labels, "transformer_tune_gb_model_report_train")

# Calculate different metrics on train_data_word_vectors
transformer_tune_gb_model_report_train = model_performance_classification_report(transformer_tune_gb_model, train_data_word_vectors, train_labels, "transformer_tune_gb_model_report_train")

# Print the dataframe transformer_tune_gb_model_report_train
print("Training performance:\n", transformer_tune_gb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                                   Model
0       1.0     1.0        1.0  1.0  transformer_tune_gb_model_report_train

# Plot the confusion matrix for the valid_data_word_vectors
confusion_matrix(transformer_tune_gb_model, valid_data_word_vectors, valid_labels, "transformer_tune_gb_model_report_valid")

# Calculate different metrics on valid_data_word_vectors
transformer_tune_gb_model_report_valid = model_performance_classification_report(transformer_tune_gb_model, valid_data_word_vectors, valid_labels, "transformer_tune_gb_model_report_valid")

# Print the dataframe transformer_tune_gb_model_report_valid
print("Validation performance:\n", transformer_tune_gb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1  \
0  0.346154  0.346154   0.315934  0.319096   

                                    Model  
0  transformer_tune_gb_model_report_valid

# Define the parameter_grid
parameter_grid = {

  # Number of boosting rounds
  "n_estimators": [50, 100, 200],

  # Step size shrinkage
  "learning_rate": [0.01, 0.1, 0.3],

  # Define max_depth
  "max_depth": [3, 5, 7],

  # Define subsample
  "subsample": [0.6, 0.8, 1.0]
}

# Initiate a XGBClassifier
transformer_tune_xgb_grid = XGBClassifier(random_state = 42)

# Initialize the grid_search
transformer_tune_xgb_grid_search = GridSearchCV(estimator = transformer_tune_xgb_grid, param_grid = parameter_grid, scoring = "f1_weighted", cv = 5, verbose = 1, n_jobs = -1)

# Train the data
transformer_tune_xgb_grid_result = transformer_tune_xgb_grid_search.fit(train_data_transformer, train_labels_encoded)

# Print the best parameters
print("Best parameters:", transformer_tune_xgb_grid_search.best_params_)

Fitting 5 folds for each of 81 candidates, totalling 405 fits
Best parameters: {'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 100, 'subsample': 0.6}

# Set the classification model to the best combination of parameters
transformer_tune_xgb_model = transformer_tune_xgb_grid_result.best_estimator_

# Fit the best algorithm to the data.
transformer_tune_xgb_model.fit(train_data_transformer, train_labels_encoded)

# Plot the confusion matrix for the train_data_transformer
confusion_matrix(transformer_tune_xgb_model, train_data_transformer, train_labels_encoded, "transformer_tune_xgb_model_report_train")

# Calculate different metrics on train_data_transformer
transformer_tune_xgb_model_report_train = model_performance_classification_report(transformer_tune_xgb_model, train_data_transformer, train_labels_encoded, "transformer_tune_xgb_model_report_train")

# Print the dataframe transformer_tune_xgb_model_report_train
print("Training performance:\n", transformer_tune_xgb_model_report_train)

Training performance:
    Accuracy  Recall  Precision   F1                                    Model
0       1.0     1.0        1.0  1.0  transformer_tune_xgb_model_report_train

# Plot the confusion matrix for the valid_data_transformer
confusion_matrix(transformer_tune_xgb_model, valid_data_transformer, valid_labels_encoded, "transformer_tune_xgb_model_report_valid")

# Calculate different metrics on valid_data_transformer
transformer_tune_xgb_model_report_valid = model_performance_classification_report(transformer_tune_xgb_model, valid_data_transformer, valid_labels_encoded, "transformer_tune_xgb_model_report_valid")

# Print the dataframe transformer_tune_xgb_model_report_valid
print("Validation performance:\n", transformer_tune_xgb_model_report_valid)

Validation performance:
    Accuracy    Recall  Precision        F1  \
0  0.461538  0.461538   0.446016  0.433572   

                                     Model  
0  transformer_tune_xgb_model_report_valid

# Compare the models and show the strengths
models_train_compare = pd.concat([
    wv_base_dtc_model_report_train,
    wv_base_rf_model_report_train,
    wv_base_ab_model_report_train,
    wv_base_gb_model_report_train,
    wv_base_xgb_model_report_train,
    glove_base_dtc_model_report_train,
    glove_base_rf_model_report_train,
    glove_base_ab_model_report_train,
    glove_base_gb_model_report_train,
    glove_base_xgb_model_report_train,
    transformer_base_dtc_model_report_train,
    transformer_base_rf_model_report_train,
    transformer_base_ab_model_report_train,
    transformer_base_gb_model_report_train,
    transformer_base_xgb_model_report_train,
    wv_tune_dtc_model_report_train,
    wv_tune_rf_model_report_train,
    wv_tune_ab_model_report_train, 
    wv_tune_gb_model_report_train,
    wv_tune_xgb_model_report_train,
    glove_tune_dtc_model_report_train,
    glove_tune_rf_model_report_train,
    glove_tune_ab_model_report_train, 
    glove_tune_gb_model_report_train,
    glove_tune_xgb_model_report_train,
    transformer_tune_dtc_model_report_train,
    transformer_tune_rf_model_report_train,
    transformer_tune_ab_model_report_train,
    transformer_tune_gb_model_report_train,
    transformer_tune_xgb_model_report_train
], axis = 0)

print("Training performance comparison:")
models_train_compare

Training performance comparison:

# Compare the models and show the strengths
models_valid_compare = pd.concat([
    wv_base_dtc_model_report_valid,
    wv_base_rf_model_report_valid,
    wv_base_ab_model_report_valid,
    wv_base_gb_model_report_valid,
    wv_base_xgb_model_report_valid,
    glove_base_dtc_model_report_valid,
    glove_base_rf_model_report_valid,
    glove_base_ab_model_report_valid,
    glove_base_gb_model_report_valid,
    glove_base_xgb_model_report_valid,
    transformer_base_dtc_model_report_valid,
    transformer_base_rf_model_report_valid,
    transformer_base_ab_model_report_valid,
    transformer_base_gb_model_report_valid,
    transformer_base_xgb_model_report_valid,
    wv_tune_dtc_model_report_valid,
    wv_tune_rf_model_report_valid,
    wv_tune_ab_model_report_valid, 
    wv_tune_xgb_model_report_valid,
    glove_tune_dtc_model_report_valid,
    glove_tune_rf_model_report_valid,
    glove_tune_ab_model_report_valid, 
    glove_tune_xgb_model_report_valid,
    transformer_tune_dtc_model_report_valid,
    transformer_tune_rf_model_report_valid,
    transformer_tune_ab_model_report_valid,
    transformer_tune_gb_model_report_valid,
    transformer_tune_xgb_model_report_valid
], axis = 0)

print("Validation performance comparison:")
models_valid_compare

Validation performance comparison:

# Plot the confusion matrix for the test_data_transformer
confusion_matrix(transformer_base_ab_model, test_data_transformer, test_labels_encoded, "transformer_base_ab_model_report_test")

# Calculate different metrics on test_data_transformer
transformer_base_ab_model_report_test = model_performance_classification_report(transformer_base_ab_model, test_data_transformer, test_labels_encoded, "transformer_base_ab_model_report_test")

# Print the dataframe transformer_base_ab_model_report_test
print("Test performance:\n", transformer_base_ab_model_report_test)

Test performance:
    Accuracy    Recall  Precision        F1  \
0  0.283019  0.283019   0.336848  0.242497   

                                   Model  
0  transformer_base_ab_model_report_test

# Create a copy of stock_news_raw into a new dataframe stock_news_anlaysis
data = stock_news_raw.copy()

# We will use the latest model from 
# https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q8_0.gguf

# Set the model_name_or_path by using the model TheBloke/Mistral-7B-Instruct-v0.2-GGU
model_name_or_path = "TheBloke/Mistral-7B-Instruct-v0.2-GGUF"

# Set the model_basename to the value mistral-7b-instruct-v0.2.Q6_K.gguf
model_basename = "mistral-7b-instruct-v0.2.Q6_K.gguf"

# Initiate a try bolock
try:

  # Use hf_hub_download to download the model mentioned earlier and store in model_path
  model_path = hf_hub_download(repo_id = model_name_or_path, filename = model_basename)

  # Print the model path
  display(Markdown("Model downloaded successfully and saved at {}.".format(model_path)))

# Catch the exception
except Exception as e:

  # Print the error
  display(Markdown("An error occurred: {}.".format(e)))

# Create the llm with Path to the model, Number of layers transferred to GPU, Context window
llm = Llama(model_path = model_path, n_gpu_layers = 100, n_ctx = 4500)

llama_model_load_from_file: using device Metal (Apple M1 Pro) - 9858 MiB free
llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from /Users/vinayakhampiholi/.cache/huggingface/hub/models--TheBloke--Mistral-7B-Instruct-v0.2-GGUF/snapshots/3a6fbf4a41a1d52e415a4958cde6856d34b2db93/mistral-7b-instruct-v0.2.Q6_K.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = llama
llama_model_loader: - kv   1:                               general.name str              = mistralai_mistral-7b-instruct-v0.2
llama_model_loader: - kv   2:                       llama.context_length u32              = 32768
llama_model_loader: - kv   3:                     llama.embedding_length u32              = 4096
llama_model_loader: - kv   4:                          llama.block_count u32              = 32
llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 14336
llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  11:                          general.file_type u32              = 18
llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv  16:                tokenizer.ggml.bos_token_id u32              = 1
llama_model_loader: - kv  17:                tokenizer.ggml.eos_token_id u32              = 2
llama_model_loader: - kv  18:            tokenizer.ggml.unknown_token_id u32              = 0
llama_model_loader: - kv  19:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  20:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  21:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  22:                    tokenizer.chat_template str              = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv  23:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   65 tensors
llama_model_loader: - type q6_K:  226 tensors
llm_load_vocab: control token:      2 '</s>' is not marked as EOG
llm_load_vocab: control token:      1 '<s>' is not marked as EOG
llm_load_vocab: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
llm_load_vocab: special tokens cache size = 3
llm_load_vocab: token to piece cache size = 0.1637 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = llama
llm_load_print_meta: vocab type       = SPM
llm_load_print_meta: n_vocab          = 32000
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 32768
llm_load_print_meta: n_embd           = 4096
llm_load_print_meta: n_layer          = 32
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 8
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_swa            = 0
llm_load_print_meta: n_embd_head_k    = 128
llm_load_print_meta: n_embd_head_v    = 128
llm_load_print_meta: n_gqa            = 4
llm_load_print_meta: n_embd_k_gqa     = 1024
llm_load_print_meta: n_embd_v_gqa     = 1024
llm_load_print_meta: f_norm_eps       = 0.0e+00
llm_load_print_meta: f_norm_rms_eps   = 1.0e-05
llm_load_print_meta: f_clamp_kqv      = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale    = 0.0e+00
llm_load_print_meta: n_ff             = 14336
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attn      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 0
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn  = 32768
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: ssm_dt_b_c_rms   = 0
llm_load_print_meta: model type       = 7B
llm_load_print_meta: model ftype      = Q6_K
llm_load_print_meta: model params     = 7.24 B
llm_load_print_meta: model size       = 5.53 GiB (6.56 BPW) 
llm_load_print_meta: general.name     = mistralai_mistral-7b-instruct-v0.2
llm_load_print_meta: BOS token        = 1 '<s>'
llm_load_print_meta: EOS token        = 2 '</s>'
llm_load_print_meta: UNK token        = 0 '<unk>'
llm_load_print_meta: PAD token        = 0 '<unk>'
llm_load_print_meta: LF token         = 13 '<0x0A>'
llm_load_print_meta: EOG token        = 2 '</s>'
llm_load_print_meta: max token length = 48
llm_load_tensors: tensor 'token_embd.weight' (q6_K) (and 0 others) cannot be used with preferred buffer type CPU_AARCH64, using CPU instead
ggml_backend_metal_log_allocated_size: allocated buffer, size =  5563.56 MiB, ( 6628.22 / 10922.67)
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading output layer to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors: Metal_Mapped model buffer size =  5563.56 MiB
llm_load_tensors:   CPU_Mapped model buffer size =   102.54 MiB
...................................................................................................
llama_new_context_with_model: n_seq_max     = 1
llama_new_context_with_model: n_ctx         = 4512
llama_new_context_with_model: n_ctx_per_seq = 4512
llama_new_context_with_model: n_batch       = 512
llama_new_context_with_model: n_ubatch      = 512
llama_new_context_with_model: flash_attn    = 0
llama_new_context_with_model: freq_base     = 1000000.0
llama_new_context_with_model: freq_scale    = 1
llama_new_context_with_model: n_ctx_per_seq (4512) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1 Pro
ggml_metal_init: picking default device: Apple M1 Pro
ggml_metal_init: using embedded metal library
ggml_metal_init: GPU name:   Apple M1 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple7  (1007)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3  (5001)
ggml_metal_init: simdgroup reduction   = true
ggml_metal_init: simdgroup matrix mul. = true
ggml_metal_init: has bfloat            = true
ggml_metal_init: use bfloat            = false
ggml_metal_init: hasUnifiedMemory      = true
ggml_metal_init: recommendedMaxWorkingSetSize  = 11453.25 MB
ggml_metal_init: loaded kernel_add                                    0x1196db8f0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_add_row                                0x38e35f320 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_sub                                    0x33e5169b0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_sub_row                                0x10129cf20 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul                                    0x33e56acc0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_row                                0x103c96f10 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_div                                    0x33e527860 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_div_row                                0x1196dbbc0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_repeat_f32                             0x38e53f3e0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_repeat_f16                             0x33b5e2050 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_repeat_i32                             0x1196dbf40 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_repeat_i16                             0x38e35f580 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_scale                                  0x38e35faf0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_scale_4                                0x38e53f950 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_clamp                                  0x1196dc630 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_tanh                                   0x33e527ac0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_relu                                   0x38e53fe30 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_sigmoid                                0x33e527d20 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_gelu                                   0x1012a1880 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_gelu_4                                 0x38e35ffd0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_gelu_quick                             0x38e360620 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_gelu_quick_4                           0x38e540310 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_silu                                   0x38e540570 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_silu_4                                 0x38e540a50 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_elu                                    0x38e540f30 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_soft_max_f16                           0x38e541190 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_soft_max_f16_4                         0x1196dcc30 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_soft_max_f32                           0x38e541900 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_soft_max_f32_4                         0x38e360880 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_diag_mask_inf                          0x1196dcfb0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_diag_mask_inf_8                        0x38e541c80 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_f32                           0x38e360ae0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_f16                           0x1196dd330 | th_max = 1024 | th_width =   32
ggml_metal_init: skipping kernel_get_rows_bf16                     (not supported)
ggml_metal_init: loaded kernel_get_rows_q4_0                          0x33e527f80 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_q4_1                          0x38e360d40 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_q5_0                          0x38e542000 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_q5_1                          0x38e3612a0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_q8_0                          0x38e361800 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_q2_K                          0x103cab4d0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_q3_K                          0x38e361b80 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_q4_K                          0x38e542380 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_q5_K                          0x320f38240 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_q6_K                          0x38e361f00 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_iq2_xxs                       0x1196dd590 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_iq2_xs                        0x38e362160 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_iq3_xxs                       0x33e528300 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_iq3_s                         0x38e3626c0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_iq2_s                         0x38e362920 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_iq1_s                         0x38e362e80 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_iq1_m                         0x320f32960 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_iq4_nl                        0x1196dd910 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_iq4_xs                        0x31fcde790 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_get_rows_i32                           0x38e3630e0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_rms_norm                               0x38e363460 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_group_norm                             0x38e3639c0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_norm                                   0x103cc9a30 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_ssm_conv_f32                           0x1012a1ae0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_ssm_scan_f32                           0x1012a1d40 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_f32_f32                         0x38e5428e0 | th_max = 1024 | th_width =   32
ggml_metal_init: skipping kernel_mul_mv_bf16_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_1row              (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_l4                (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_bf16                  (not supported)
ggml_metal_init: loaded kernel_mul_mv_f16_f32                         0x33e528680 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_f16_f32_1row                    0x33e528a00 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_f16_f32_l4                      0x38e542e40 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_f16_f16                         0x38e363d40 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_q4_0_f32                        0x33e528d80 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_q4_1_f32                        0x38e3642a0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_q5_0_f32                        0x320f376d0 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_q5_1_f32                        0x103cc9c90 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_q8_0_f32                        0x38e364500 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_f16_f32_r1_2                0x38e5431c0 | th_max =  896 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_f16_f32_r1_3                0x38e543420 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_f16_f32_r1_4                0x38e364cd0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_f16_f32_r1_5                0x38e364f30 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_0_f32_r1_2               0x38e543980 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_0_f32_r1_3               0x38e3652b0 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_0_f32_r1_4               0x38e543be0 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_0_f32_r1_5               0x38e543f60 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_1_f32_r1_2               0x38e5444c0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_1_f32_r1_3               0x38e365630 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_1_f32_r1_4               0x38e544720 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_1_f32_r1_5               0x1012a1fa0 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_0_f32_r1_2               0x38e544aa0 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_0_f32_r1_3               0x38e544e20 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_0_f32_r1_4               0x38e545380 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_0_f32_r1_5               0x38e5455e0 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_1_f32_r1_2               0x38e545960 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_1_f32_r1_3               0x33e529100 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_1_f32_r1_4               0x33e529480 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_1_f32_r1_5               0x38e545ce0 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q8_0_f32_r1_2               0x38e546060 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q8_0_f32_r1_3               0x38e3659b0 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q8_0_f32_r1_4               0x38e365f10 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q8_0_f32_r1_5               0x33e529800 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_K_f32_r1_2               0x38e5465c0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_K_f32_r1_3               0x38e366170 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_K_f32_r1_4               0x38e546940 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q4_K_f32_r1_5               0x38e546ba0 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_K_f32_r1_2               0x38e3664f0 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_K_f32_r1_3               0x38e366870 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_K_f32_r1_4               0x38e366bf0 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q5_K_f32_r1_5               0x38e366f70 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q6_K_f32_r1_2               0x33e529b80 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q6_K_f32_r1_3               0x38e3672f0 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q6_K_f32_r1_4               0x38e546f20 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_q6_K_f32_r1_5               0x1196dde70 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_iq4_nl_f32_r1_2             0x38e5472a0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_iq4_nl_f32_r1_3             0x38e367670 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_iq4_nl_f32_r1_4             0x38e547800 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_ext_iq4_nl_f32_r1_5             0x38e367bd0 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_q2_K_f32                        0x38e547b80 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_q3_K_f32                        0x1012a2200 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_q4_K_f32                        0x1012a2460 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_q5_K_f32                        0x38e367e30 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_q6_K_f32                        0x38e3681b0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_iq2_xxs_f32                     0x38e547de0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_iq2_xs_f32                      0x38e368530 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_iq3_xxs_f32                     0x38e3688b0 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_iq3_s_f32                       0x38e368e10 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_iq2_s_f32                       0x103cca010 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_iq1_s_f32                       0x38e548160 | th_max =  448 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_iq1_m_f32                       0x38e369070 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_iq4_nl_f32                      0x31fcac030 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_iq4_xs_f32                      0x38e3695d0 | th_max =  896 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_f32_f32                      0x38e369950 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_f16_f32                      0x1196de0d0 | th_max = 1024 | th_width =   32
ggml_metal_init: skipping kernel_mul_mv_id_bf16_f32                (not supported)
ggml_metal_init: loaded kernel_mul_mv_id_q4_0_f32                     0x38e5484e0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_q4_1_f32                     0x38e369bb0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_q5_0_f32                     0x38e548860 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_q5_1_f32                     0x38e369f30 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_q8_0_f32                     0x38e36a2b0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_q2_K_f32                     0x38e548be0 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_q3_K_f32                     0x33e529f00 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_q4_K_f32                     0x38e36a810 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_q5_K_f32                     0x38e548f60 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_q6_K_f32                     0x103cca390 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_iq2_xxs_f32                  0x38e5492e0 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_iq2_xs_f32                   0x38e549540 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_iq3_xxs_f32                  0x38e5498c0 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_iq3_s_f32                    0x1196de330 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_iq2_s_f32                    0x1196de890 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_iq1_s_f32                    0x1012a26c0 | th_max =  448 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_iq1_m_f32                    0x38e36ab90 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_iq4_nl_f32                   0x38e549f40 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mv_id_iq4_xs_f32                   0x1196deaf0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_f32_f32                         0x38e36b210 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_f16_f32                         0x320f2f5e0 | th_max =  832 | th_width =   32
ggml_metal_init: skipping kernel_mul_mm_bf16_f32                   (not supported)
ggml_metal_init: loaded kernel_mul_mm_q4_0_f32                        0x38e36b470 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_q4_1_f32                        0x1012a2920 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_q5_0_f32                        0x1196dee70 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_q5_1_f32                        0x38e36b9d0 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_q8_0_f32                        0x38e36bd50 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_q2_K_f32                        0x38e36c2b0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_q3_K_f32                        0x1196df1f0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_q4_K_f32                        0x38e54a1a0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_q5_K_f32                        0x38e54a400 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_q6_K_f32                        0x1012a2b80 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_iq2_xxs_f32                     0x1012a36a0 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_iq2_xs_f32                      0x1196df750 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_iq3_xxs_f32                     0x38e54a780 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_iq3_s_f32                       0x38e54ab00 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_iq2_s_f32                       0x38e54b060 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_iq1_s_f32                       0x38e54b3e0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_iq1_m_f32                       0x38e54b760 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_iq4_nl_f32                      0x38e36c510 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_iq4_xs_f32                      0x38e54bae0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_f32_f32                      0x38e54bd40 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_f16_f32                      0x38e36ca70 | th_max =  832 | th_width =   32
ggml_metal_init: skipping kernel_mul_mm_id_bf16_f32                (not supported)
ggml_metal_init: loaded kernel_mul_mm_id_q4_0_f32                     0x38e36cdf0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_q4_1_f32                     0x38e36d050 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_q5_0_f32                     0x38e36d3d0 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_q5_1_f32                     0x38e36d750 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_q8_0_f32                     0x38e36dad0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_q2_K_f32                     0x1196dfad0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_q3_K_f32                     0x38e36e030 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_q4_K_f32                     0x38e54c0c0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_q5_K_f32                     0x1196dfe50 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_q6_K_f32                     0x1196e01d0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_iq2_xxs_f32                  0x38e36e290 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_iq2_xs_f32                   0x38e36e610 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_iq3_xxs_f32                  0x38e54c440 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_iq3_s_f32                    0x1196e0430 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_iq2_s_f32                    0x1196e0690 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_iq1_s_f32                    0x38e36eab0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_iq1_m_f32                    0x38e36ee30 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_iq4_nl_f32                   0x38e36f1b0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_mul_mm_id_iq4_xs_f32                   0x38e36f530 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_rope_norm_f32                          0x38e54cac0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_rope_norm_f16                          0x38e36f8b0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_rope_neox_f32                          0x1196e0a10 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_rope_neox_f16                          0x38e54cd20 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_im2col_f16                             0x38e36fc30 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_im2col_f32                             0x1012a3900 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_im2col_ext_f16                         0x38e370190 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_im2col_ext_f32                         0x38e3703f0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_conv_transpose_1d_f32_f32              0x33e52a280 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_conv_transpose_1d_f16_f32              0x38e54d0a0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_upscale_f32                            0x38e54d600 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_pad_f32                                0x1196e0eb0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_pad_reflect_1d_f32                     0x38e54d980 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_timestep_embedding_f32                 0x38e54dbe0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_arange_f32                             0x38e54e140 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_argsort_f32_i32_asc                    0x38e54e4c0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_argsort_f32_i32_desc                   0x1196e1410 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_leaky_relu_f32                         0x38e370a80 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_f16_h64                 0x38e54e720 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_f16_h80                 0x38e54ec80 | th_max =  640 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_f16_h96                 0x38e370e00 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_f16_h112                0x38e371180 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_f16_h128                0x38e54f000 | th_max =  512 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_f16_h256                0x38e371500 | th_max =  512 | th_width =   32
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h64           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h80           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h96           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h112          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h128          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h256          (not supported)
ggml_metal_init: loaded kernel_flash_attn_ext_q4_0_h64                0x38e371880 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q4_0_h80                0x38e371c00 | th_max =  896 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q4_0_h96                0x38e54f690 | th_max =  896 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q4_0_h112               0x38e372160 | th_max =  896 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q4_0_h128               0x38e3723c0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q4_0_h256               0x1012f3bb0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q4_1_h64                0x1196e1670 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q4_1_h80                0x38e372740 | th_max =  896 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q4_1_h96                0x38e372ac0 | th_max =  896 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q4_1_h112               0x38e372e40 | th_max =  896 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q4_1_h128               0x38e54f8f0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q4_1_h256               0x38e54fe50 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_0_h64                0x1196e19f0 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_0_h80                0x38e5500b0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_0_h96                0x38e3733a0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_0_h112               0x38e550430 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_0_h128               0x38e550990 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_0_h256               0x1012f4110 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_1_h64                0x38e550bf0 | th_max =  576 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_1_h80                0x38e373720 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_1_h96                0x1012f4370 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_1_h112               0x1012f46f0 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_1_h128               0x1196e1d70 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q5_1_h256               0x38e373980 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q8_0_h64                0x38e550f70 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q8_0_h80                0x38e373d00 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q8_0_h96                0x1196e20f0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q8_0_h112               0x1196e2650 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q8_0_h128               0x38e374080 | th_max =  896 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_q8_0_h256               0x38e5514d0 | th_max =  896 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_vec_f16_h128            0x38e3745e0 | th_max = 1024 | th_width =   32
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h128      (not supported)
ggml_metal_init: loaded kernel_flash_attn_ext_vec_q4_0_h128           0x38e374960 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_vec_q4_1_h128           0x38e551850 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_vec_q5_0_h128           0x1196e28b0 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_vec_q5_1_h128           0x38e374bc0 | th_max =  768 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_vec_q8_0_h128           0x38e551bd0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_vec_f16_h256            0x38e374f40 | th_max = 1024 | th_width =   32
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h256      (not supported)
ggml_metal_init: loaded kernel_flash_attn_ext_vec_q4_0_h256           0x38e3752c0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_vec_q4_1_h256           0x38e551e30 | th_max =  896 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_vec_q5_0_h256           0x38e5521b0 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_vec_q5_1_h256           0x38e375820 | th_max =  704 | th_width =   32
ggml_metal_init: loaded kernel_flash_attn_ext_vec_q8_0_h256           0x38e375a80 | th_max =  832 | th_width =   32
ggml_metal_init: loaded kernel_set_f32                                0x38e375e00 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_set_i32                                0x38e552530 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_cpy_f32_f32                            0x38e376180 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_cpy_f32_f16                            0x38e552a90 | th_max = 1024 | th_width =   32
ggml_metal_init: skipping kernel_cpy_f32_bf16                      (not supported)
ggml_metal_init: loaded kernel_cpy_f16_f32                            0x38e552cf0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_cpy_f16_f16                            0x38e376500 | th_max = 1024 | th_width =   32
ggml_metal_init: skipping kernel_cpy_bf16_f32                      (not supported)
ggml_metal_init: skipping kernel_cpy_bf16_bf16                     (not supported)
ggml_metal_init: loaded kernel_cpy_f32_q8_0                           0x1012f4a70 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_cpy_f32_q4_0                           0x1196e2c30 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_cpy_f32_q4_1                           0x38e376950 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_cpy_f32_q5_0                           0x38e553070 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_cpy_f32_q5_1                           0x38e376cd0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_cpy_f32_iq4_nl                         0x38e377230 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_concat                                 0x38e3775b0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_sqr                                    0x1196e2e90 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_sqrt                                   0x103cca710 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_sin                                    0x3494c2230 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_cos                                    0x359cd0240 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_sum_rows                               0x38e377bb0 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_argmax                                 0x38e378110 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_pool_2d_avg_f32                        0x38e378490 | th_max = 1024 | th_width =   32
ggml_metal_init: loaded kernel_pool_2d_max_f32                        0x38e3786f0 | th_max = 1024 | th_width =   32
llama_kv_cache_init: kv_size = 4512, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 32, can_shift = 1
llama_kv_cache_init: layer 0: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 1: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 2: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 3: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 4: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 5: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 6: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 7: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 8: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 9: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 10: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 11: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 12: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 13: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 14: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 15: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 16: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 17: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 18: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 19: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 20: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 21: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 22: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 23: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 24: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 25: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 26: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 27: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 28: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 29: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 30: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init: layer 31: n_embd_k_gqa = 1024, n_embd_v_gqa = 1024
llama_kv_cache_init:      Metal KV buffer size =   564.00 MiB
llama_new_context_with_model: KV self size  =  564.00 MiB, K (f16):  282.00 MiB, V (f16):  282.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.12 MiB
llama_new_context_with_model:      Metal compute buffer size =   322.82 MiB
llama_new_context_with_model:        CPU compute buffer size =    16.82 MiB
llama_new_context_with_model: graph nodes  = 1030
llama_new_context_with_model: graph splits = 2
Metal : EMBED_LIBRARY = 1 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | DOTPROD = 1 | LLAMAFILE = 1 | ACCELERATE = 1 | AARCH64_REPACK = 1 | 
Model metadata: {'general.quantization_version': '2', 'tokenizer.chat_template': "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}", 'tokenizer.ggml.add_eos_token': 'false', 'tokenizer.ggml.add_bos_token': 'true', 'tokenizer.ggml.padding_token_id': '0', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'tokenizer.ggml.bos_token_id': '1', 'tokenizer.ggml.model': 'llama', 'llama.attention.head_count_kv': '8', 'llama.context_length': '32768', 'llama.attention.head_count': '32', 'llama.rope.freq_base': '1000000.000000', 'llama.rope.dimension_count': '128', 'general.file_type': '18', 'llama.feed_forward_length': '14336', 'llama.embedding_length': '4096', 'llama.block_count': '32', 'general.architecture': 'llama', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'general.name': 'mistralai_mistral-7b-instruct-v0.2'}
Available chat formats from metadata: chat_template.default
Guessed chat format: mistral-instruct

# Convert the 'Date' column to datetime format.
data["Date"] = pd.to_datetime(data["Date"])

# Group the data by week using the 'Date' column.
weekly_grouped = data.groupby(pd.Grouper(key = "Date", freq = "W"))

# Join the news values with ' || ' separator.
weekly_grouped = weekly_grouped.agg({"News": lambda x: " || ".join(x)}).reset_index()

# Display the weekly_grouped information
display(Markdown("**Observatiions:**\n- Shape of weekly_grouped is: {}.".format(weekly_grouped.shape)))

# Check the entries in the weekly_grouped
weekly_grouped

# creating a copy of the data
data_1 = weekly_grouped.copy()

# Check the first row
news = data_1.loc[0, "News"]

# Check the length of news
print(len(news.split(" ")))

2611

# Print the news
print(news)

 The tech sector experienced a significant decline in the aftermarket following Apple's Q1 revenue warning. Notable suppliers, including Skyworks, Broadcom, Lumentum, Qorvo, and TSMC, saw their stocks drop in response to Apple's downward revision of its revenue expectations for the quarter, previously announced in January. ||  Apple lowered its fiscal Q1 revenue guidance to $84 billion from earlier estimates of $89-$93 billion due to weaker than expected iPhone sales. The announcement caused a significant drop in Apple's stock price and negatively impacted related suppliers, leading to broader market declines for tech indices such as Nasdaq 10 ||  Apple cut its fiscal first quarter revenue forecast from $89-$93 billion to $84 billion due to weaker demand in China and fewer iPhone upgrades. CEO Tim Cook also mentioned constrained sales of Airpods and Macbooks. Apple's shares fell 8.5% in post market trading, while Asian suppliers like Hon ||  This news article reports that yields on long-dated U.S. Treasury securities hit their lowest levels in nearly a year on January 2, 2019, due to concerns about the health of the global economy following weak economic data from China and Europe, as well as the partial U.S. government shutdown. Apple ||  Apple's revenue warning led to a decline in USD JPY pair and a gain in Japanese yen, as investors sought safety in the highly liquid currency. Apple's underperformance in Q1, with forecasted revenue of $84 billion compared to analyst expectations of $91.5 billion, triggered risk aversion mood in markets || Apple CEO Tim Cook discussed the company's Q1 warning on CNBC, attributing US-China trade tensions as a factor. Despite not mentioning iPhone unit sales specifically, Cook indicated Apple may comment on them again. Services revenue is projected to exceed $10.8 billion in Q1. Cook also addressed the lack of ||  Roku Inc has announced plans to offer premium video channels on a subscription basis through its free streaming service, The Roku Channel. Partners include CBS Corp's Showtime, Lionsgate's Starz, and Viacom Inc's Noggin. This model follows Amazon's successful Channels business, which generated an estimated ||  Wall Street saw modest gains on Wednesday but were threatened by fears of a global economic slowdown following Apple's shocking revenue forecast cut, blaming weak demand in China. The tech giant's suppliers and S&P 500 futures also suffered losses. Reports of decelerating factory activity in China and the euro zone ||  Apple's fiscal first quarter revenue came in below analysts' estimates at around $84 billion, a significant drop from the forecasted range of $89-$93 billion. The tech giant attributed the shortfall to lower iPhone revenue and upgrades, as well as weakness in emerging markets. Several brokerages had already reduced their production estimates ||  Apple Inc. lowered its quarterly sales forecast for the fiscal first quarter, underperforming analysts' expectations due to slowing Chinese economy and trade tensions. The news sent Apple shares tumbling and affected Asia-listed suppliers like Hon Hai Precision Industry Co Ltd, Taiwan Semiconductor Manufacturing Company, and LG Innot ||  The Australian dollar experienced significant volatility on Thursday, plunging to multi-year lows against major currencies due to automated selling, liquidity issues, and a drought of trades. The largest intra-day falls in the Aussie's history occurred amid violent movements in AUD/JPY and AUD/ ||  In early Asian trading on Thursday, the Japanese yen surged as the U.S. dollar and Australian dollar collapsed in thin markets due to massive stop loss sales triggered by Apple's earnings warning of sluggish iPhone sales in China and risk aversion. The yen reached its lowest levels against the U.S. dollar since March  ||  The dollar fell from above 109 to 106.67 after Apple's revenue warning, while the 10-year Treasury yield also dropped to 2.61%. This followed money flowing into US government paper. Apple's shares and U.S. stock index futures declined, with the NAS ||  RBC Capital maintains its bullish stance on Apple, keeping its Outperform rating and $220 price target. However, analyst Amit Daryanani warns of ongoing iPhone demand concerns, which could impact pricing power and segmentation efforts if severe. He suggests potential capital allocation adjustments if the stock underperforms for several quarters ||  Oil prices dropped on Thursday as investor sentiment remained affected by China's economic slowdown and turmoil in stock and currency markets. US WTI Crude Oil fell by $2.10 to $45.56 a barrel, while International Brent Oil was down $1.20 at $54.26 ||  In this news article, investors' concerns about a slowing Chinese and global economy, amplified by Apple's revenue warning, led to a significant surge in the Japanese yen. The yen reached its biggest one-day rise in 20 months, with gains of over 4% versus the dollar. This trend was driven by automated ||  In Asia, gold prices rose to over six-month highs on concerns of a global economic slowdown and stock market volatility. Apple lowered its revenue forecast for the first quarter, leading Asian stocks to decline and safe haven assets like gold and Japanese yen to gain. Data showed weakened factory activity in Asia, particularly China, adding to ||  Fears of a global economic slowdown led to a decline in the US dollar on Thursday, as the yen gained ground due to its status as a safe haven currency. The USD index slipped below 96, and USD JPY dropped to 107.61, while the yen strengthened by 4.4%. ||  In Thursday trading, long-term US Treasury yields dropped significantly below 2.6%, reaching levels not seen in over a year, as investors shifted funds from stocks to bonds following Apple's warning of decreased revenue due to emerging markets and China's impact on corporate profits, with the White House advisor adding to concerns of earnings down ||  Gold prices have reached their highest level since mid-June, with the yellow metal hitting $1,291.40 per ounce due to investor concerns over a slowing economy and Apple's bearish revenue outlook. Saxo Bank analyst Ole Hansen predicts gold may reach $1,300 sooner ||  Wedbush analyst Daniel Ives lowered his price target for Apple from $275 to $200 due to concerns over potential iPhone sales stagnation, with an estimated 750 million active iPhones worldwide that could cease growing or even decline. He maintains an Outperform rating and remains bullish on the long ||  Oil prices rebounded on Thursday due to dollar weakness, signs of output cuts by Saudi Arabia, and weaker fuel oil margins leading Riyadh to lower February prices for heavier crude grades sold to Asia. The Organization of the Petroleum Exporting Countries (OPEC) led by Saudi Arabia and other producers ||  This news article reports on the impact of Apple's Q1 revenue warning on several tech and biotech stocks. Sesen Bio (SESN) and Prana Biotechnology (PRAN) saw their stock prices drop by 28% and 11%, respectively, following the announcement. Mellanox Technologies (ML ||  Gold prices reached within $5 of $1,300 on Thursday as weak stock markets and a slumping dollar drove investors towards safe-haven assets. The U.S. stock market fell about 2%, with Apple's rare profit warning adding to investor unease. COMEX gold futures settled at $1 ||  The FDIC Chair, Jelena McWilliams, expressed no concern over market volatility affecting the U.S banking system due to banks' ample capital. She also mentioned a review of the CAMELS rating system used to evaluate bank health for potential inconsistencies and concerns regarding forum shopping. This review comes from industry ||  Apple cut its quarterly revenue forecast for the first time in over 15 years due to weak iPhone sales in China, representing around 20% of Apple's revenue. This marks a significant downturn during Tim Cook's tenure and reflects broader economic concerns in China exacerbated by trade tensions with the US. U ||  Goldman analyst Rod Hall lowered his price target for Apple from $182 to $140, citing potential risks to the tech giant's 2019 numbers due to uncertainties in Chinese demand. He reduced his revenue estimate for the year by $6 billion and EPS forecast by $1.54 ||  Delta Air Lines lowered its fourth-quarter revenue growth forecast to a range of 3% from the previous estimate of 3% to 5%. Earnings per share are now expected to be $1.25 to $1.30. The slower pace of improvement in late December was unexpected, and Delta cited this as ||  Apple's profit warning has significantly impacted the stock market and changed the outlook for interest rates. The chance of a rate cut in May has increased to 15-16% from just 3%, according to Investing com's Fed Rate Monitor Tool. There is even a 1% chance of two cuts in May. ||  The White House advisor, Kevin Hassett, stated that a decline in Chinese economic growth would negatively impact U.S. firm profits but recover once a trade deal is reached between Washington and Beijing. He also noted that Asian economies, including China, have been experiencing significant slowdowns since last spring due to U.S. tariffs ||  The White House economic adviser, Kevin Hassett, warned that more companies could face earnings downgrades due to ongoing trade negotiations between the U.S. and China, leading to a decline in oil prices on Thursday. WTI crude fell 44 cents to $44.97 a barrel, while Brent crude inched ||  Japanese stocks suffered significant losses on the first trading day of 2019, with the Nikkei 225 and Topix indices both falling over 3 percent. Apple's revenue forecast cut, citing weak iPhone sales in China, triggered global growth concerns and sent technology shares tumbling. The S&P 50 ||  Investors withdrew a record $98 billion from U.S. stock funds in December, with fears of aggressive monetary policy and an economic slowdown driving risk reduction. The S&P 500 fell 9% last month, with some seeing declines as a buying opportunity. Apple's warning of weak iPhone sales added ||  Apple's Q1 revenue guidance cut, resulting from weaker demand in China, led to an estimated $3.8 billion paper loss for Berkshire Hathaway due to its $252 million stake in Apple. This news, coupled with broad market declines, caused a significant $21.4 billion decrease in Berk ||  This news article reports that a cybersecurity researcher, Wish Wu, planned to present at the Black Hat Asia hacking conference on how to bypass Apple's Face ID biometric security on iPhones. However, his employer, Ant Financial, which operates Alipay and uses facial recognition technologies including Face ID, asked him to withdraw ||  OPEC's production cuts faced uncertainty as oil prices were influenced by volatile stock markets, specifically due to Apple's lowered revenue forecast and global economic slowdown fears. US WTI and Brent crude both saw gains, but these were checked by stock market declines. Shale production is expected to continue impacting the oil market in ||  Warren Buffett's Berkshire Hathaway suffered significant losses in the fourth quarter due to declines in Apple, its largest common stock investment. Apple cut its revenue forecast, causing a 5-6% decrease in Berkshire's Class A shares. The decline resulted in potential unrealized investment losses and could push Berk ||  This news article reports that on Thursday, the two-year Treasury note yield dropped below the Federal Reserve's effective rate for the first time since 2008. The market move suggests investors believe the Fed will not be able to continue tightening monetary policy. The drop in yields was attributed to a significant decline in U.S ||  The U.S. and China will hold their first face-to-face trade talks since agreeing to a 90-day truce in their trade war last month. Deputy U.S. Trade Representative Jeffrey Gerrish will lead the U.S. delegation for negotiations on Jan. 7 and 8, ||  Investors bought gold in large quantities due to concerns over a global economic slowdown, increased uncertainty in the stock market, and potential Fed rate hikes. The precious metal reached its highest price since June, with gold ETF holdings also seeing significant increases. Factors contributing to this demand include economic downturn, central bank policy mistakes, and ||  Delta Air Lines Inc reported lower-than-expected fourth quarter unit revenue growth, citing weaker than anticipated late bookings and increased competition. The carrier now expects total revenue per available seat mile to rise about 3 percent in the period, down from its earlier forecast of 3.5 percent growth. Fuel prices are also expected to ||  U.S. stocks experienced significant declines on Thursday as the S&P 500 dropped over 2%, the Dow Jones Industrial Average fell nearly 3%, and the Nasdaq Composite lost approximately 3% following a warning of weak revenue from Apple and indications of slowing U.S. factory activity, raising concerns ||  President Trump expressed optimism over potential trade talks with China, citing China's current economic weakness as a potential advantage for the US. This sentiment was echoed by recent reports of weakened demand for Apple iPhones in China, raising concerns about the overall health of the Chinese economy. The White House is expected to take a strong stance in ||  Qualcomm secured a court order in Germany banning the sale of some iPhone models due to patent infringement, leading Apple to potentially remove these devices from its stores. However, third-party resellers like Gravis continue selling the affected iPhones. This is the third major effort by Qualcomm to ban Apple's iPhones glob ||  Oil prices rose on Friday in Asia as China confirmed trade talks with the U.S., with WTI gaining 0.7% to $47.48 and Brent increasing 0.7% to $56.38 a barrel. The gains came after China's Commerce Ministry announced that deputy U.S. Trade ||  Gold prices surged past the psychologically significant level of $1,300 per ounce in Asia on Friday due to growing concerns over a potential global economic downturn. The rise in gold was attributed to weak PMI data from China and Apple's reduced quarterly sales forecast. Investors viewed gold as a safe haven asset amidst ||  In an internal memo, Huawei's Chen Lifang reprimanded two employees for sending a New Year greeting on the company's official Twitter account using an iPhone instead of a Huawei device. The incident caused damage to the brand and was described as a "blunder" in the memo. The mistake occurred due to ||  This news article reports on the positive impact of trade war talks between Beijing and Washington on European stock markets, specifically sectors sensitive to the trade war such as carmakers, industrials, mining companies, and banking. Stocks rallied with mining companies leading the gains due to copper price recovery. Bayer shares climbed despite a potential ruling restricting || Amazon has sold over 100 million devices with its Alexa digital assistant, according to The Verge. The company is cautious about releasing hardware sales figures and did not disclose holiday numbers for the Echo Dot. Over 150 products feature Alexa, and more than 28,000 smart home || The Supreme Court will review Broadcom's appeal in a shareholder lawsuit over the 2015 acquisition of Emulex. The case hinges on whether intent to defraud is required for such lawsuits, and the decision could extend beyond the Broadcom suit. An Emulex investor filed a class action lawsuit ||  The Chinese central bank announced a fifth reduction in the required reserve ratio (RRR) for banks, freeing up approximately 116.5 billion yuan for new lending. This follows mounting concerns about China's economic health amid slowing domestic demand and U.S. tariffs on exports. Premier Li Keqiang || The stock market rebounded strongly on Friday following positive news about US-China trade talks, a better-than-expected jobs report, and dovish comments from Federal Reserve Chairman Jerome Powell. The Dow Jones Industrial Average rose over 746 points, with the S&P 500 and Nasdaq Com

display(Markdown("**Observations:**\n- Columns in model_response_parsed:{}".format(data_1.columns)))

prompt = """
You are an expert data analyst specializing in stock market news article analysis that affects the financial market.
Task: Analyze the news headlines and determine which news articles are positive, negative, or neutral in sentiment.
Instructions:
1. Read the individual news items and identify if the article contains positive or negative sentiment.
2. Group the articles by week and consider the date which has maximum articles.
3. If there are more than one date having equal number of articles then choose the older date.
4. For the selected date, list 3 articles that have highest positive sentiments and list 3 articles that have highest negative sentiments in the articles of week.
5. If there are more than 3 articles, then choose any 3 articles randomly.
6. Prepare the output in JSON format with columns 'Week End Date', 'Week Positive Events', 'Week Negative Events'. 
7. Validate the JSON format and correct the errors if the JSON is not valid. 
8. Return the output in the valid JSON format.
Remove any null or NaN values from the output and do not append anything after the end of JSON.
"""

# Generate the summary using response_mistral_1 with prompt and news
summary = response_mistral_1(prompt, news)

llama_perf_context_print:        load time =   23059.02 ms
llama_perf_context_print: prompt eval time =       0.00 ms /  4007 tokens (    0.00 ms per token,      inf tokens per second)
llama_perf_context_print:        eval time =       0.00 ms /   379 runs   (    0.00 ms per token,      inf tokens per second)
llama_perf_context_print:       total time =   49392.73 ms /  4386 tokens

# Display the summary
summary

' {\n         "Week End Date": "01-07-2019",\n         "Week Positive Events": [\n           {\n             "Title": "Roku Inc announces plans to offer premium video channels on a subscription basis through its free streaming service, The Roku Channel.",\n             "Sentiment": "Positive"\n           },\n           {\n             "Title": "FDIC Chair, Jelena McWilliams, expressed no concern over market volatility affecting the U.S banking system due to banks\' ample capital.",\n             "Sentiment": "Positive"\n           },\n           {\n             "Title": "Oil prices rebounded on Thursday due to dollar weakness, signs of output cuts by Saudi Arabia, and weaker fuel oil margins leading Riyadh to lower February prices for heavier crude grades sold to Asia.",\n             "Sentiment": "Positive"\n           }\n         ],\n         "Week Negative Events": [\n           {\n             "Title": "Apple cut its quarterly revenue forecast for the first time in over 15 years due to weak iPhone sales in China.",\n             "Sentiment": "Negative"\n           },\n           {\n             "Title": "Investors withdrew a record $98 billion from U.S. stock funds in December, with fears of aggressive monetary policy and an economic slowdown driving risk reduction.",\n             "Sentiment": "Negative"\n           },\n           {\n             "Title": "Delta Air Lines reported lower-than-expected fourth quarter unit revenue growth, citing weaker than anticipated late bookings and increased competition.",\n             "Sentiment": "Negative"\n           }\n         ]\n       }'

extract_json_data(summary)

{'Week End Date': '01-07-2019',
 'Week Positive Events': [{'Title': 'Roku Inc announces plans to offer premium video channels on a subscription basis through its free streaming service, The Roku Channel.',
   'Sentiment': 'Positive'},
  {'Title': "FDIC Chair, Jelena McWilliams, expressed no concern over market volatility affecting the U.S banking system due to banks' ample capital.",
   'Sentiment': 'Positive'},
  {'Title': 'Oil prices rebounded on Thursday due to dollar weakness, signs of output cuts by Saudi Arabia, and weaker fuel oil margins leading Riyadh to lower February prices for heavier crude grades sold to Asia.',
   'Sentiment': 'Positive'}],
 'Week Negative Events': [{'Title': 'Apple cut its quarterly revenue forecast for the first time in over 15 years due to weak iPhone sales in China.',
   'Sentiment': 'Negative'},
  {'Title': 'Investors withdrew a record $98 billion from U.S. stock funds in December, with fears of aggressive monetary policy and an economic slowdown driving risk reduction.',
   'Sentiment': 'Negative'},
  {'Title': 'Delta Air Lines reported lower-than-expected fourth quarter unit revenue growth, citing weaker than anticipated late bookings and increased competition.',
   'Sentiment': 'Negative'}]}

# Create a new column Key Events to capture the summary
data_1["Key Events"] = data_1["News"].progress_apply(lambda x: response_mistral_1(prompt, x))

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# Format the Key Events using json extract
data_1["model_response_parsed"] = data_1["Key Events"].apply(extract_json_data)

# Print the first 5 records
data_1.head()

Error parsing JSON: Extra data: line 33 column 8 (char 1303)
Error parsing JSON: Expecting ',' delimiter: line 28 column 14 (char 1100)

# Format the response by normalizing the output
model_response_parsed = pd.json_normalize(data_1["model_response_parsed"])

# Print the first 5 records
model_response_parsed.head()

# Display the columns in the model_response_parsed
display(Markdown("Columns in model_response_parsed: {}.".format(model_response_parsed.columns)))

# Display the columns in data_1
display(Markdown("{}".format(data_1.columns)))

# Use the above data to prepare the dataframe as final_output
final_output = pd.concat([data_1.reset_index(drop = True),model_response_parsed],axis = 1)

# Drop the unwanted columns
final_output.drop(["Date", "News", "Key Events", "model_response_parsed"], axis = 1, inplace = True)

# Display the dataframe
final_output

	Date	News	Open	High	Low	Close	Volume	Label
0	2019-01-02	The tech sector experienced a significant dec...	41.740002	42.244999	41.482498	40.246914	130672400	-1
1	2019-01-02	Apple lowered its fiscal Q1 revenue guidance ...	41.740002	42.244999	41.482498	40.246914	130672400	-1
2	2019-01-02	Apple cut its fiscal first quarter revenue fo...	41.740002	42.244999	41.482498	40.246914	130672400	-1
3	2019-01-02	This news article reports that yields on long...	41.740002	42.244999	41.482498	40.246914	130672400	-1
4	2019-01-02	Apple's revenue warning led to a decline in U...	41.740002	42.244999	41.482498	40.246914	130672400	-1

	count	mean	std	min	25%	50%	75%	max
Open	349.0	4.622923e+01	6.442817e+00	3.756750e+01	4.174000e+01	4.597500e+01	5.070750e+01	6.681750e+01
High	349.0	4.670046e+01	6.507321e+00	3.781750e+01	4.224500e+01	4.602500e+01	5.085000e+01	6.706250e+01
Low	349.0	4.574539e+01	6.391976e+00	3.730500e+01	4.148250e+01	4.564000e+01	4.977750e+01	6.586250e+01
Close	349.0	4.492632e+01	6.398338e+00	3.625413e+01	4.024691e+01	4.459692e+01	4.911079e+01	6.480523e+01
Volume	349.0	1.289482e+08	4.317031e+07	4.544800e+07	1.032720e+08	1.156272e+08	1.511252e+08	2.444392e+08
Label	349.0	-5.444126e-02	7.151192e-01	-1.000000e+00	-1.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00

	Date	News	Open	High	Low	Close	Volume	Label	Label_Meaning
0	2019-01-02	The tech sector experienced a significant dec...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative
1	2019-01-02	Apple lowered its fiscal Q1 revenue guidance ...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative
2	2019-01-02	Apple cut its fiscal first quarter revenue fo...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative
3	2019-01-02	This news article reports that yields on long...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative
4	2019-01-02	Apple's revenue warning led to a decline in U...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative
5	2019-01-02	Apple CEO Tim Cook discussed the company's Q1 ...	41.740002	42.244999	41.482498	40.246914	130672400	0	Neutral
6	2019-01-02	Roku Inc has announced plans to offer premium...	41.740002	42.244999	41.482498	40.246914	130672400	1	Positive
7	2019-01-02	Wall Street saw modest gains on Wednesday but...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative
8	2019-01-02	Apple's fiscal first quarter revenue came in ...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative
9	2019-01-02	Apple Inc. lowered its quarterly sales foreca...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative

	Date	News	Open	High	Low	Close	Volume	Label	Label_Meaning	News_Length
0	2019-01-02	The tech sector experienced a significant dec...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	324
1	2019-01-02	Apple lowered its fiscal Q1 revenue guidance ...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	323
2	2019-01-02	Apple cut its fiscal first quarter revenue fo...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	296
3	2019-01-02	This news article reports that yields on long...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	300
4	2019-01-02	Apple's revenue warning led to a decline in U...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	305
5	2019-01-02	Apple CEO Tim Cook discussed the company's Q1 ...	41.740002	42.244999	41.482498	40.246914	130672400	0	Neutral	307
6	2019-01-02	Roku Inc has announced plans to offer premium...	41.740002	42.244999	41.482498	40.246914	130672400	1	Positive	308
7	2019-01-02	Wall Street saw modest gains on Wednesday but...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	317
8	2019-01-02	Apple's fiscal first quarter revenue came in ...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	344
9	2019-01-02	Apple Inc. lowered its quarterly sales foreca...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	336

	Date	News	Open	High	Low	Close	Volume	Label	Label_Meaning	News_Length	Day	Month	Year
0	2019-01-02	The tech sector experienced a significant dec...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	324	2	1	2019
1	2019-01-02	Apple lowered its fiscal Q1 revenue guidance ...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	323	2	1	2019
2	2019-01-02	Apple cut its fiscal first quarter revenue fo...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	296	2	1	2019
3	2019-01-02	This news article reports that yields on long...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	300	2	1	2019
4	2019-01-02	Apple's revenue warning led to a decline in U...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	305	2	1	2019
5	2019-01-02	Apple CEO Tim Cook discussed the company's Q1 ...	41.740002	42.244999	41.482498	40.246914	130672400	0	Neutral	307	2	1	2019
6	2019-01-02	Roku Inc has announced plans to offer premium...	41.740002	42.244999	41.482498	40.246914	130672400	1	Positive	308	2	1	2019
7	2019-01-02	Wall Street saw modest gains on Wednesday but...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	317	2	1	2019
8	2019-01-02	Apple's fiscal first quarter revenue came in ...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	344	2	1	2019
9	2019-01-02	Apple Inc. lowered its quarterly sales foreca...	41.740002	42.244999	41.482498	40.246914	130672400	-1	Negative	336	2	1	2019

	Date	News	Open	High	Low	Close	Volume	News_Length	Day	Month	Year	News_Cleaned
140	2019-01-29	Apple, Aetna, and CVS are collaborating on a ...	39.062500	39.532501	38.527500	37.385262	166348800	320	29	1	2019	appl aetna cv collabor new health app call att...
83	2019-01-14	The U.S. stock market declined on Monday as c...	37.712502	37.817501	37.305000	36.254131	129756800	337	14	1	2019	u stock market declin monday concern global ec...
6	2019-01-02	Roku Inc has announced plans to offer premium...	41.740002	42.244999	41.482498	40.246914	130672400	308	2	1	2019	roku inc announc plan offer premium video chan...
294	2019-04-04	In response to the deadly mosque shootings in...	48.697498	49.092499	48.285000	47.499989	76457200	354	4	4	2019	respons deadli mosqu shoot christchurch austra...
90	2019-01-15	The Lynx Equity Strategies analysts, KC Rajku...	37.567501	38.347500	37.512501	36.996128	114843600	319	15	1	2019	lynx equiti strategi analyst kc rajkumar jahan...
81	2019-01-11	Apple's NASDAQ AAPL stock declined by 0.52% i...	62.384998	63.982498	62.290001	62.571354	151125200	282	11	1	2019	appl nasdaq aapl stock declin 0 52 premarket t...
13	2019-01-02	RBC Capital maintains its bullish stance on A...	41.740002	42.244999	41.482498	40.246914	130672400	347	2	1	2019	rbc capit maintain bullish stanc appl keep out...
18	2019-01-03	In Thursday trading, long-term US Treasury yi...	43.570000	43.787498	43.222500	42.470604	103544800	350	3	1	2019	thursday trade long term us treasuri yield dro...
124	2019-01-23	FireEye's stock price surged after Baird adde...	38.537498	38.785000	37.924999	37.201569	92522400	276	23	1	2019	fireey stock price surg baird ad fresh pick li...
297	2019-04-04	Apple has slashed the price of its iPhone XR ...	48.697498	49.092499	48.285000	47.499989	76457200	332	4	4	2019	appl slash price iphon xr model india approxim...

	Date	News	Open	High	Low	Close	Volume	News_Length	Day	Month	Year	News_Cleaned
43	2019-01-04	Qualcomm secured a court order in Germany ban...	47.910000	47.919998	47.095001	46.419842	111448000	329	4	1	2019	qualcomm secur court order germani ban sale ip...
213	2019-02-25	Sony, aiming to differentiate itself in the s...	43.540001	43.967499	43.487499	42.290981	87493600	302	25	2	2019	soni aim differenti smartphon market unveil ne...
323	2019-04-18	In an effort to address EU antitrust concerns...	50.779999	51.037498	50.630001	49.483093	96783200	344	18	4	2019	effort address eu antitrust concern avoid pena...
242	2019-03-15	Apple and Spotify are in a dispute with EU an...	46.212502	46.832500	45.935001	45.177055	156171600	316	15	3	2019	appl spotifi disput eu antitrust regul alleg a...
96	2019-01-15	Record-breaking online sales of $126 billion ...	37.567501	38.347500	37.512501	36.996128	114843600	258	15	1	2019	record break onlin sale 126 billion 2018 u hol...
32	2019-01-03	Investors withdrew a record $98 billion from ...	43.570000	43.787498	43.222500	42.470604	103544800	291	3	1	2019	investor withdrew record 98 billion u stock fu...
51	2019-01-04	The stock market rebounded strongly on Friday ...	47.910000	47.919998	47.095001	46.419842	111448000	285	4	1	2019	stock market rebound strongli friday follow po...
78	2019-01-11	Several Chinese retailers, including Alibaba-...	62.384998	63.982498	62.290001	62.571354	151125200	261	11	1	2019	sever chines retail includ alibaba back sune j...
77	2019-01-11	Wall Street experienced a decline after the o...	62.384998	63.982498	62.290001	62.571354	151125200	241	11	1	2019	wall street experienc declin open bell friday ...
291	2019-04-03	The S&P 500, Dow Jones Industrial Average, an...	43.922501	44.437500	43.492500	42.684212	109744800	288	3	4	2019	p 500 dow jone industri averag nasdaq composit...

	Date	News	Open	High	Low	Close	Volume	News_Length	Day	Month	Year	News_Cleaned
104	2019-01-16	Veteran producer Jason Katims is leaving Univ...	38.270000	38.970001	38.250000	37.448109	122278800	304	16	1	2019	veteran produc jason katim leav univers tv joi...
5	2019-01-02	Apple CEO Tim Cook discussed the company's Q1 ...	41.740002	42.244999	41.482498	40.246914	130672400	307	2	1	2019	appl ceo tim cook discuss compani q1 warn cnbc...
21	2019-01-03	Oil prices rebounded on Thursday due to dolla...	43.570000	43.787498	43.222500	42.470604	103544800	303	3	1	2019	oil price rebound thursday due dollar weak sig...
159	2019-01-30	Apple plans to cut prices of some flagship iP...	40.812500	41.537498	40.057499	39.939968	244439200	328	30	1	2019	appl plan cut price flagship iphon boost sale ...
315	2019-04-17	Facebook, aiming to compete with Alexa, Siri,...	49.884998	50.845001	49.652500	49.305897	115627200	335	17	4	2019	facebook aim compet alexa siri googl assist de...
89	2019-01-14	The Dow Jones Industrial Average and S&P 500 ...	37.712502	37.817501	37.305000	36.254131	129756800	328	14	1	2019	dow jone industri averag p 500 end lower monda...
306	2019-04-15	Amazon's potential entry into the free music ...	49.645000	49.962502	49.502499	48.359261	70146400	329	15	4	2019	amazon potenti entri free music stream market ...
206	2019-02-21	Apple's long-rumored vehicle project may shif...	42.950001	43.092499	42.575001	41.521526	68998800	286	21	2	2019	appl long rumor vehicl project may shift devel...
19	2019-01-03	Gold prices have reached their highest level ...	43.570000	43.787498	43.222500	42.470604	103544800	263	3	1	2019	gold price reach highest level sinc mid june y...
52	2019-01-07	Sprint and Samsung plan to release 5G smartph...	50.792500	51.122501	50.162498	49.110790	109012000	290	7	1	2019	sprint samsung plan releas 5g smartphon nine u...

	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	...	Feature 290	Feature 291	Feature 292	Feature 293	Feature 294	Feature 295	Feature 296	Feature 297	Feature 298	Feature 299
0	0.000010	0.004452	-0.000803	0.002290	-0.000040	-0.005234	0.002488	0.007884	0.001792	-0.001035	...	-0.000072	0.005193	0.003902	0.001080	0.003638	0.003960	0.000450	-0.002698	0.002935	-0.000112
1	0.000320	0.005409	-0.001305	0.002215	0.000254	-0.006231	0.003779	0.010415	0.002598	-0.001126	...	-0.000510	0.006814	0.005342	0.000555	0.004394	0.005849	0.000602	-0.002808	0.004028	-0.000031
2	0.000452	0.003109	-0.000670	0.001689	0.000300	-0.003721	0.001130	0.006135	0.001794	-0.000641	...	0.000312	0.004133	0.003265	-0.000395	0.003305	0.003079	-0.000241	-0.001782	0.002479	0.000155
3	-0.000122	0.002064	-0.000202	0.001148	-0.000234	-0.002616	0.001289	0.004496	0.000364	-0.000198	...	0.000351	0.002098	0.002649	0.000135	0.000870	0.002044	0.000430	-0.001615	0.001728	0.000057
4	0.000288	0.003650	-0.000616	0.001747	-0.000221	-0.005244	0.002226	0.007146	0.001318	-0.000944	...	-0.000057	0.004638	0.004224	0.000952	0.003186	0.004536	0.000026	-0.002623	0.003039	0.000332
5	0.000493	0.004593	-0.001094	0.003167	0.000113	-0.006796	0.003612	0.010291	0.002135	-0.001510	...	-0.000095	0.006537	0.005146	0.000419	0.004556	0.005609	0.001037	-0.003678	0.004344	-0.000669
6	-0.000077	0.003046	-0.000745	0.001517	-0.000362	-0.004261	0.002095	0.007087	0.001043	-0.001300	...	-0.000149	0.005019	0.003754	0.000972	0.003207	0.004198	0.000536	-0.002068	0.003085	0.000435
7	0.000696	0.004895	-0.001419	0.002747	-0.000177	-0.006322	0.003146	0.010103	0.002267	-0.001367	...	-0.000546	0.007500	0.005491	0.000746	0.004606	0.006100	0.000812	-0.002608	0.003417	-0.000538
8	0.000397	0.003967	-0.000849	0.001838	-0.000674	-0.005187	0.002540	0.008271	0.002360	-0.001262	...	-0.000379	0.005932	0.004533	0.001019	0.003891	0.004852	-0.000088	-0.002509	0.002884	-0.000270
9	0.000030	0.003235	-0.000339	0.001958	-0.000466	-0.004333	0.001860	0.006759	0.001400	-0.000733	...	0.000304	0.004366	0.003725	0.000288	0.002983	0.004509	0.000792	-0.003008	0.002945	-0.000052

	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	...	Feature 290	Feature 291	Feature 292	Feature 293	Feature 294	Feature 295	Feature 296	Feature 297	Feature 298	Feature 299
0	0.000196	0.004406	-0.000737	0.002425	-0.000281	-0.004770	0.002318	0.007529	0.001618	-0.000645	...	-0.000468	0.004922	0.004028	0.000264	0.002939	0.004628	0.000763	-0.003536	0.003382	-0.000105
1	0.000533	0.001654	-0.000656	0.001560	0.000229	-0.003068	0.001506	0.004665	0.001141	-0.000200	...	-0.000050	0.002713	0.002788	0.000179	0.001659	0.002696	0.000221	-0.001402	0.002163	-0.000110
2	0.000323	0.002687	0.000107	0.001723	0.000192	-0.003388	0.001548	0.005341	0.001359	-0.000354	...	-0.000380	0.003447	0.003444	0.000364	0.002852	0.003420	0.000246	-0.001370	0.001823	-0.000449
3	0.000595	0.004317	-0.000577	0.003183	-0.001067	-0.005687	0.002410	0.009250	0.001839	-0.000635	...	0.000345	0.005569	0.004755	0.000438	0.004478	0.005255	0.000795	-0.003046	0.003668	-0.000477
4	0.000595	0.003271	-0.000404	0.001641	-0.000318	-0.003615	0.001917	0.006079	0.002029	-0.000111	...	-0.000062	0.004078	0.003216	0.000494	0.002740	0.003874	0.000523	-0.001374	0.002422	0.000657
5	0.000421	0.004629	-0.000774	0.002116	-0.000556	-0.005710	0.003428	0.008219	0.002412	-0.000915	...	-0.000367	0.006513	0.004505	0.000099	0.004214	0.005915	0.001017	-0.002292	0.002982	0.000014
6	0.000122	0.004142	-0.000655	0.002630	0.000057	-0.006289	0.003746	0.009548	0.001784	-0.000446	...	-0.000500	0.007158	0.004995	0.000805	0.004626	0.005039	0.000753	-0.002946	0.004016	-0.000443
7	0.000245	0.004487	-0.000808	0.002233	-0.000754	-0.005845	0.002940	0.008821	0.001555	-0.001098	...	0.000135	0.005964	0.003910	-0.000004	0.003938	0.004810	0.001037	-0.002987	0.003365	-0.000371
8	-0.000312	0.003292	-0.000296	0.001936	0.000255	-0.004413	0.003117	0.007124	0.002221	-0.000351	...	0.000093	0.005445	0.003602	0.000714	0.003678	0.003717	0.000488	-0.002274	0.003708	-0.000062
9	0.000066	0.004852	-0.000632	0.002579	-0.000642	-0.005961	0.003386	0.008958	0.002092	-0.000817	...	-0.000065	0.006573	0.004640	0.000551	0.003976	0.005148	0.000876	-0.003189	0.003850	0.000556

	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	...	Feature 290	Feature 291	Feature 292	Feature 293	Feature 294	Feature 295	Feature 296	Feature 297	Feature 298	Feature 299
0	0.000170	0.002765	-0.000248	0.001259	-0.000296	-0.003120	0.001450	0.005063	0.000268	-0.000675	...	-0.000035	0.003634	0.003417	0.000534	0.001902	0.002973	0.000051	-0.002164	0.001789	-0.000137
1	0.000594	0.004430	-0.001113	0.002693	-0.000537	-0.006120	0.002860	0.009568	0.001959	-0.000773	...	-0.000577	0.006502	0.005031	0.000504	0.004279	0.005193	0.000349	-0.003117	0.003442	-0.000020
2	0.000465	0.002375	-0.000940	0.001047	-0.000639	-0.004046	0.002037	0.005107	0.001170	-0.000928	...	0.000168	0.004533	0.002526	-0.000380	0.002944	0.003451	0.000789	-0.001868	0.002626	-0.000225
3	-0.000028	0.004959	-0.000890	0.002570	-0.000969	-0.006425	0.003443	0.009962	0.001897	-0.000977	...	-0.000189	0.006523	0.004828	0.000039	0.004150	0.005882	0.000727	-0.003465	0.004178	0.000047
4	-0.000214	0.002614	0.000032	0.001943	0.000334	-0.003041	0.001601	0.004717	0.001107	-0.000333	...	-0.000520	0.002914	0.002658	0.000303	0.001747	0.002692	0.001023	-0.001225	0.002035	-0.000062
5	0.000211	0.005228	-0.000410	0.002596	-0.000056	-0.006728	0.004146	0.010992	0.001904	-0.001251	...	-0.000086	0.008079	0.005685	0.000578	0.004829	0.007086	0.000659	-0.003063	0.005027	0.000043
6	-0.000250	0.004149	-0.001253	0.002221	0.000076	-0.005002	0.002752	0.006988	0.001709	-0.000799	...	-0.000268	0.005091	0.003605	0.000558	0.003441	0.004013	0.000333	-0.002438	0.002357	-0.000124
7	0.000402	0.002573	-0.000130	0.001474	-0.000471	-0.003485	0.001476	0.005566	0.000794	-0.000672	...	-0.000602	0.003265	0.003231	-0.000049	0.002099	0.003054	0.000217	-0.001207	0.002970	0.000062
8	0.000274	0.003717	-0.000916	0.002729	-0.000719	-0.005152	0.002003	0.007534	0.001153	-0.000792	...	0.000061	0.004405	0.003711	0.000337	0.003070	0.004017	0.000364	-0.002098	0.002638	0.000188
9	0.000305	0.002032	-0.000630	0.000892	-0.000647	-0.003688	0.001819	0.004682	0.000822	-0.000383	...	0.000244	0.003503	0.002514	0.000778	0.001808	0.002464	0.000348	-0.001638	0.001886	0.000405

	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	...	Feature 90	Feature 91	Feature 92	Feature 93	Feature 94	Feature 95	Feature 96	Feature 97	Feature 98	Feature 99
0	-0.152654	0.146622	0.065633	-0.072611	-0.039109	0.020089	0.034112	0.174098	-0.010602	0.060106	...	0.026438	0.137168	0.022047	-0.244009	-0.402092	0.029641	0.083717	-0.199961	0.515118	-0.010270
1	0.200185	0.386240	0.323972	0.048430	-0.071983	-0.676247	-0.034530	-0.051528	-0.049674	-0.194433	...	-0.015247	0.233471	-0.227676	-0.095122	-0.664772	0.287383	0.203437	-0.300613	0.597282	-0.205631
2	-0.023604	-0.286361	0.052732	-0.142494	0.341926	-0.304393	0.085680	0.044406	0.089239	-0.108030	...	-0.074636	0.198720	-0.119618	-0.158043	-0.149637	-0.141126	0.099189	-0.202402	0.418310	0.123389
3	-0.042903	-0.027410	0.103281	-0.307101	0.232164	0.233166	-0.164887	0.108429	-0.174721	0.171346	...	0.053440	0.040548	0.070971	-0.150723	-0.283743	0.077095	-0.164428	-0.132156	0.511897	-0.008165
4	-0.083469	0.090211	0.218926	-0.244540	-0.123161	-0.222490	0.058415	-0.003647	0.008824	0.041252	...	-0.098991	0.004717	-0.113477	-0.170185	-0.249488	0.012636	0.150453	-0.183086	0.306597	0.120947
5	0.192237	0.225896	0.195350	-0.012229	-0.002406	-0.364938	0.053131	0.023022	-0.216707	-0.100083	...	0.012913	0.239475	-0.104386	-0.076843	-0.406662	0.219512	0.038956	-0.091629	0.248335	0.004875
6	-0.125647	0.053197	0.118191	-0.049376	-0.303660	-0.498806	-0.340967	-0.153026	-0.094119	0.065621	...	-0.132457	0.219120	-0.132913	-0.428219	-0.384401	0.089270	0.290785	-0.192258	0.209205	0.239004
7	0.037556	0.243848	0.187096	0.122443	-0.028455	-0.424736	-0.099776	-0.102172	-0.168743	-0.181081	...	-0.079724	0.172838	-0.189481	-0.162482	-0.398279	0.176663	0.091000	-0.172395	0.332701	-0.011535
8	-0.064055	0.299827	0.124545	-0.219903	-0.105825	-0.473509	-0.039510	-0.137434	-0.268899	-0.014142	...	-0.054450	0.232886	-0.149146	-0.261217	-0.601270	0.178685	0.072390	-0.134420	0.306289	0.144923
9	-0.068715	0.129224	0.147463	0.069562	0.084400	-0.343430	-0.011723	0.032834	0.055968	0.123824	...	-0.143814	0.187928	0.030268	-0.291802	-0.269417	0.030087	0.126206	-0.001467	0.444312	-0.094643

	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	...	Feature 90	Feature 91	Feature 92	Feature 93	Feature 94	Feature 95	Feature 96	Feature 97	Feature 98	Feature 99
0	0.134596	-0.038992	0.076920	0.015381	0.077577	-0.134198	-0.006193	-0.020873	-0.065827	0.024788	...	-0.081416	-0.136629	-0.164778	-0.005019	-0.128306	-0.006245	-0.008041	-0.048903	0.420255	-0.082204
1	0.144286	-0.029698	0.160753	0.100328	0.110714	-0.202173	0.002417	0.145035	-0.096344	0.113436	...	0.177624	0.280935	-0.027660	0.067890	-0.332986	-0.007275	-0.032473	-0.200236	0.376195	0.017565
2	0.043477	0.164411	0.180311	0.001211	0.211069	-0.057315	-0.128467	-0.075606	-0.001398	-0.008225	...	0.003861	0.357044	-0.172806	-0.211713	-0.226617	0.007507	-0.049127	-0.142469	0.542164	0.055076
3	0.022282	0.223521	0.143935	-0.136300	-0.015211	-0.033227	0.082051	-0.003373	0.366549	0.045459	...	0.102662	0.138309	-0.130315	-0.258266	-0.321312	0.104324	0.100930	0.077341	0.640605	-0.071270
4	0.264090	0.266007	0.335878	-0.152134	0.204135	-0.149030	0.239887	0.319550	-0.319458	-0.049135	...	-0.045470	0.288627	-0.185054	-0.223563	-0.637795	0.058044	0.003293	0.073055	0.522899	-0.313171
5	0.159789	0.339053	0.215880	-0.017613	-0.128788	-0.220963	-0.039570	-0.108553	-0.100723	-0.137623	...	-0.093649	0.201742	-0.016132	-0.182184	-0.472115	0.201094	0.253860	-0.035450	0.412966	-0.145836
6	0.034067	0.087187	0.207606	-0.120134	-0.160224	-0.472653	-0.107733	-0.092472	-0.057484	-0.278265	...	0.308092	0.157673	-0.151199	-0.107924	-0.400429	0.206932	0.123851	-0.217051	0.286330	0.300103
7	0.049114	0.085808	0.021939	0.054403	-0.036588	-0.258329	-0.185148	-0.040757	0.124111	0.001907	...	0.091997	0.393608	-0.085967	-0.199623	-0.282693	-0.029366	0.027511	-0.096356	0.292816	-0.129465
8	0.094400	0.344174	0.210820	0.040378	-0.067761	-0.063022	0.195827	0.123302	-0.470543	-0.232314	...	-0.163419	0.242558	-0.162451	0.105544	-0.798050	0.073607	0.089183	-0.089736	0.311381	0.057143
9	0.115102	0.163432	0.182335	0.100149	-0.073633	-0.333058	0.003820	0.140227	-0.187404	-0.102124	...	0.171030	0.234243	-0.173784	0.174500	-0.382641	0.186450	0.070045	-0.322816	0.269534	-0.060846

	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	...	Feature 90	Feature 91	Feature 92	Feature 93	Feature 94	Feature 95	Feature 96	Feature 97	Feature 98	Feature 99
0	0.044340	0.183891	0.118931	-0.278109	-0.010284	-0.157264	0.040982	0.050363	-0.242641	0.007637	...	0.047502	0.187090	-0.112852	-0.074601	-0.224148	-0.065702	-0.012471	0.037446	0.364606	0.071186
1	-0.142790	0.077274	0.221093	-0.084383	-0.017716	-0.328716	-0.073703	0.064571	0.057792	-0.048042	...	0.003168	0.173722	-0.040743	-0.213238	-0.235741	0.205431	0.055332	-0.222766	0.326911	-0.014639
2	0.149237	0.407745	0.347759	-0.151761	0.206105	-0.962671	-0.365348	0.134783	-0.221627	0.154342	...	-0.109960	-0.030941	-0.415537	-0.308708	-0.853869	0.145317	0.119042	-0.327625	0.517852	-0.204387
3	0.063703	0.361517	0.200947	0.000156	-0.049097	-0.458565	-0.132471	-0.059976	-0.064913	0.061094	...	-0.015782	0.189809	-0.167364	-0.182017	-0.677354	-0.085671	0.169417	-0.151520	0.511334	-0.018108
4	-0.174913	-0.070321	0.094371	-0.126588	0.228459	-0.105147	-0.055601	-0.015346	0.266499	0.081719	...	0.000195	0.192502	0.102294	-0.126720	-0.003931	-0.178598	-0.098765	-0.355908	0.380207	0.063025
5	0.133085	0.237077	0.251213	0.099616	-0.122877	-0.605397	-0.152318	-0.098548	-0.234632	-0.135584	...	0.052568	0.174015	-0.178455	-0.183650	-0.448350	0.202496	0.369583	-0.177379	0.558654	0.150614
6	-0.049729	0.090223	0.279184	-0.025733	0.423677	-0.217360	-0.109712	-0.052040	0.077628	-0.133844	...	0.190936	0.158197	-0.211871	-0.183057	-0.353535	0.147569	-0.047477	0.015981	0.472397	0.308866
7	-0.088047	0.114142	0.026959	-0.102723	0.061906	-0.312858	0.046035	0.038012	0.273326	0.185969	...	0.098769	-0.193596	-0.067450	0.027359	-0.122915	0.161385	-0.357966	-0.195069	0.345772	-0.068469
8	-0.013889	0.146491	0.133993	-0.060445	-0.045259	-0.430659	-0.016421	0.018458	-0.236445	-0.031562	...	-0.074861	0.290114	-0.140112	-0.016861	-0.414231	0.172855	-0.032562	-0.069898	0.248968	-0.184480
9	0.217768	0.085190	0.194953	-0.154768	0.197371	-0.096574	0.218177	0.084204	-0.311771	0.042371	...	0.078874	-0.064231	-0.059965	-0.087535	-0.404929	0.085872	-0.085496	-0.030691	0.351350	0.069088

	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	...	Feature 374	Feature 375	Feature 376	Feature 377	Feature 378	Feature 379	Feature 380	Feature 381	Feature 382	Feature 383
0	-0.110148	-0.001813	-0.021451	-0.002667	0.046194	0.056637	0.002792	0.005426	-0.008668	0.003443	...	0.000853	0.037577	0.033752	-0.105064	-0.042527	0.053359	0.129411	-0.106390	-0.007181	0.020372
1	0.071291	0.043437	0.134944	0.050140	0.062604	-0.004043	0.023534	0.002085	0.019136	-0.035864	...	0.037719	0.017399	-0.016022	0.062520	-0.068315	-0.083288	-0.045739	-0.070933	-0.041311	0.060020
2	-0.021218	-0.136285	-0.058848	0.025848	0.108263	0.045691	-0.019474	-0.015968	0.083321	-0.008238	...	-0.005728	-0.002553	0.019535	-0.056822	-0.028219	0.035990	-0.052221	-0.037376	0.044406	0.044798
3	0.040308	-0.019457	-0.021917	-0.035726	0.125325	0.048073	-0.023694	-0.092761	0.044787	0.077105	...	0.025340	-0.002032	0.129696	0.042910	-0.091924	0.055233	0.061209	-0.079704	0.067386	-0.027997
4	0.011473	-0.039363	0.096462	-0.034529	-0.037259	0.005808	0.002305	0.013453	0.023828	0.046716	...	0.042436	-0.001705	0.003244	-0.047366	-0.060567	0.054412	0.038742	-0.147563	0.054540	-0.008501
5	0.002829	-0.036285	0.024775	0.016081	-0.052090	0.029207	-0.028368	0.030125	0.072204	0.023590	...	-0.019699	-0.006518	-0.063953	-0.028164	-0.048638	-0.010105	0.038472	-0.128154	-0.009793	0.082032
6	-0.006174	-0.100343	-0.012478	0.058953	0.007695	0.006640	0.045239	0.059621	0.027641	0.023641	...	0.043411	-0.031559	-0.033221	-0.040372	-0.080294	0.051134	0.018036	-0.147158	-0.016850	0.050764
7	0.012811	-0.010161	0.011493	0.066230	0.051739	-0.004206	-0.062686	0.073841	-0.011468	-0.003516	...	-0.014657	0.027993	-0.053503	0.012063	-0.087461	-0.067544	-0.055144	-0.085454	0.056617	0.104927
8	-0.058224	-0.094512	-0.045841	0.022870	0.022759	0.018077	0.143291	-0.003413	-0.002288	0.014574	...	0.004058	0.013884	0.011323	-0.047922	-0.047336	-0.032166	0.044618	-0.121346	-0.080534	0.068414
9	0.000172	0.029570	0.061788	-0.032502	0.032001	-0.012613	-0.004792	0.030688	0.132195	-0.028486	...	0.016201	-0.001477	-0.024361	0.020713	-0.070790	-0.005634	0.043669	-0.132004	-0.027160	0.039506

	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	...	Feature 374	Feature 375	Feature 376	Feature 377	Feature 378	Feature 379	Feature 380	Feature 381	Feature 382	Feature 383
0	-0.043499	-0.001613	0.054336	-0.093748	-0.039390	0.017204	0.028205	-0.004828	0.065845	0.001980	...	-0.051773	0.005689	0.035528	-0.045088	0.013758	-0.008080	0.096066	-0.123451	0.088598	0.086363
1	-0.028311	-0.000311	0.039248	-0.087238	0.039234	-0.013966	-0.023713	0.099197	-0.025709	0.040571	...	0.061237	-0.036462	0.079572	0.037790	-0.020490	0.078982	0.099366	-0.016849	-0.015311	0.098239
2	-0.035504	-0.016100	0.063776	-0.110819	-0.029443	-0.007032	-0.018075	-0.017862	-0.015859	-0.032729	...	0.057371	-0.040132	0.026570	-0.118910	-0.067383	0.014524	0.023684	-0.044002	0.060234	0.042352
3	0.020668	-0.035789	0.099880	-0.012302	0.056883	0.046200	0.033811	0.022351	0.079521	0.025272	...	0.027925	0.036220	-0.009832	-0.072616	0.011464	0.078088	0.057168	-0.086072	0.109614	0.058841
4	0.025471	-0.040586	-0.027107	-0.022850	-0.000948	0.003815	-0.023832	0.057765	0.025158	-0.018203	...	0.002983	-0.005037	0.046696	-0.051669	-0.001666	0.013861	0.013453	-0.179749	0.004540	0.088829
5	0.078979	0.000280	0.056514	0.055993	0.088708	0.064288	0.061378	0.039501	0.050269	-0.016071	...	-0.006491	-0.008424	-0.020985	-0.023571	-0.085368	-0.081781	-0.015627	-0.158213	-0.029616	0.062977
6	-0.036447	-0.133283	0.019591	0.127448	0.055454	0.029426	0.056759	-0.057176	-0.034122	-0.080772	...	0.020479	0.050963	-0.085262	0.033345	-0.072607	-0.064098	0.066937	-0.064834	-0.108639	0.035658
7	-0.024089	0.024757	0.059358	-0.031739	0.038419	-0.019097	0.006093	0.070280	0.077963	-0.024917	...	0.012556	0.024287	-0.013641	-0.004929	-0.044054	0.027268	0.037757	-0.160795	-0.026713	0.031812
8	0.060930	-0.031267	0.046723	0.071941	-0.003221	0.080210	-0.020842	0.008297	-0.000554	-0.047834	...	0.010458	0.018670	-0.030662	0.053090	-0.063115	-0.046960	-0.036750	-0.116560	-0.109784	0.083244
9	0.058919	-0.079949	-0.036240	0.037580	0.031521	-0.042483	0.007612	0.025869	-0.061133	0.017782	...	0.041163	-0.017368	0.002693	0.020381	-0.101120	-0.038255	0.063546	-0.067177	-0.084070	-0.001594

	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	...	Feature 374	Feature 375	Feature 376	Feature 377	Feature 378	Feature 379	Feature 380	Feature 381	Feature 382	Feature 383
0	-0.066343	-0.122110	-0.001810	-0.045346	-0.024339	0.040346	-0.007698	-0.041012	0.031532	-0.058215	...	0.020142	-0.027505	0.032753	-0.033259	-0.059884	-0.015427	0.077444	-0.004905	0.054434	0.014731
1	-0.012017	-0.009333	0.047045	-0.012058	-0.048513	0.003005	0.088060	0.016712	0.073103	-0.012077	...	-0.022428	-0.040182	0.035312	-0.024627	-0.007340	0.001820	0.033593	-0.098873	0.069781	0.003399
2	-0.080865	-0.091732	0.077949	0.083578	0.063496	-0.021900	-0.092165	0.016674	-0.012971	-0.122080	...	0.024138	-0.057888	-0.062509	0.070929	-0.046363	-0.050982	0.028135	-0.052306	-0.052416	0.070809
3	-0.011648	0.039082	0.088509	-0.002131	0.013176	-0.020711	-0.004946	0.026473	0.087968	-0.006440	...	-0.001196	0.005530	-0.039604	-0.045868	-0.028963	0.019595	0.043193	-0.140436	0.038888	0.056085
4	-0.117553	-0.063399	0.004084	-0.064281	-0.004937	-0.031909	-0.027201	-0.097004	-0.003450	-0.048137	...	0.030749	-0.027896	0.098429	-0.033369	0.017551	-0.009053	0.030952	0.040301	0.053879	-0.038022
5	-0.003247	0.041219	0.076850	0.062036	0.047053	-0.017339	-0.019722	0.023111	-0.040158	-0.022682	...	-0.011602	-0.021623	-0.017353	0.090085	-0.129029	-0.082654	-0.031851	-0.079409	-0.066840	0.068146
6	-0.037600	-0.119075	0.006264	0.022886	0.097723	0.088301	0.031577	-0.025522	0.074915	0.005525	...	0.063152	0.019771	-0.051171	-0.022808	-0.010288	0.103174	0.030504	-0.098180	0.034563	0.078408
7	-0.054160	0.043325	0.071062	-0.010540	0.070889	-0.048881	-0.019981	0.006571	0.021180	0.030897	...	0.018999	-0.018757	-0.063354	-0.090051	-0.034137	-0.014588	0.055771	0.006917	0.023100	0.061962
8	-0.022372	-0.054762	0.033843	0.059857	0.011938	-0.075037	0.071523	0.070326	0.008512	-0.002186	...	0.000176	-0.007236	-0.036764	0.033041	-0.092175	0.009964	0.092022	-0.195363	-0.056212	0.056036
9	0.035011	-0.026610	0.091551	0.005726	0.004388	-0.008184	-0.059526	-0.015192	-0.021839	0.064880	...	0.040210	-0.085234	0.035377	-0.094639	0.051172	0.018321	0.000968	-0.029849	0.011811	0.084456

Accuracy	Recall	Precision	F1	Model
1.000000	1.000000	1.000000	1.000000	wv_base_dtc_model_report_train
1.000000	1.000000	1.000000	1.000000	wv_base_rf_model_report_train
0.790984	0.790984	0.800990	0.789920	wv_base_ab_model_report_train
1.000000	1.000000	1.000000	1.000000	wv_base_gb_model_report_train
1.000000	1.000000	1.000000	1.000000	wv_base_xgb_model_report_train
1.000000	1.000000	1.000000	1.000000	glove_base_dtc_model_report_train
1.000000	1.000000	1.000000	1.000000	glove_base_rf_model_report_train
0.848361	0.848361	0.849216	0.848412	glove_base_ab_model_report_train
1.000000	1.000000	1.000000	1.000000	glove_base_gb_model_report_train
1.000000	1.000000	1.000000	1.000000	glove_base_xgb_model_report_train
1.000000	1.000000	1.000000	1.000000	transformer_base_dtc_model_report_train
1.000000	1.000000	1.000000	1.000000	transformer_base_rf_model_report_train
0.799180	0.799180	0.805554	0.799190	transformer_base_ab_model_report_train
1.000000	1.000000	1.000000	1.000000	transformer_base_gb_model_report_train
1.000000	1.000000	1.000000	1.000000	transformer_base_xgb_model_report_train
0.909836	0.909836	0.924380	0.910022	wv_tune_dtc_model_report_train
1.000000	1.000000	1.000000	1.000000	wv_tune_rf_model_report_train
0.758197	0.758197	0.800528	0.750878	wv_tune_ab_model_report_train
1.000000	1.000000	1.000000	1.000000	wv_tune_gb_model_report_train
1.000000	1.000000	1.000000	1.000000	wv_tune_xgb_model_report_train
0.909836	0.909836	0.924380	0.910022	glove_tune_dtc_model_report_train
1.000000	1.000000	1.000000	1.000000	glove_tune_rf_model_report_train
0.848361	0.848361	0.849216	0.848412	glove_tune_ab_model_report_train
1.000000	1.000000	1.000000	1.000000	glove_tune_gb_model_report_train
1.000000	1.000000	1.000000	1.000000	glove_tune_xgb_model_report_train
0.909836	0.909836	0.924380	0.910022	transformer_tune_dtc_model_report_train
1.000000	1.000000	1.000000	1.000000	transformer_tune_rf_model_report_train
0.868852	0.868852	0.873107	0.868837	transformer_tune_ab_model_report_train
1.000000	1.000000	1.000000	1.000000	transformer_tune_gb_model_report_train
1.000000	1.000000	1.000000	1.000000	transformer_tune_xgb_model_report_train

Accuracy	Recall	Precision	F1	Model
0.423077	0.423077	0.407925	0.411658	wv_base_dtc_model_report_valid
0.423077	0.423077	0.310651	0.352850	wv_base_rf_model_report_valid
0.423077	0.423077	0.356937	0.360662	wv_base_ab_model_report_valid
0.384615	0.384615	0.371300	0.357311	wv_base_gb_model_report_valid
0.423077	0.423077	0.421105	0.385216	wv_base_xgb_model_report_valid
0.480769	0.480769	0.488905	0.480008	glove_base_dtc_model_report_valid
0.519231	0.519231	0.621248	0.456832	glove_base_rf_model_report_valid
0.365385	0.365385	0.382784	0.371118	glove_base_ab_model_report_valid
0.365385	0.365385	0.337865	0.349288	glove_base_gb_model_report_valid
0.442308	0.442308	0.382692	0.389252	glove_base_xgb_model_report_valid
0.403846	0.403846	0.391765	0.394776	transformer_base_dtc_model_report_valid
0.403846	0.403846	0.335623	0.329668	transformer_base_rf_model_report_valid
0.519231	0.519231	0.505845	0.495774	transformer_base_ab_model_report_valid
0.403846	0.403846	0.394591	0.384258	transformer_base_gb_model_report_valid
0.442308	0.442308	0.415335	0.412475	transformer_base_xgb_model_report_valid
0.365385	0.365385	0.341963	0.351474	wv_tune_dtc_model_report_valid
0.480769	0.480769	0.466828	0.422205	wv_tune_rf_model_report_valid
0.519231	0.519231	0.505601	0.479741	wv_tune_ab_model_report_valid
0.461538	0.461538	0.491124	0.424279	wv_tune_xgb_model_report_valid
0.365385	0.365385	0.341963	0.351474	glove_tune_dtc_model_report_valid
0.480769	0.480769	0.466828	0.422205	glove_tune_rf_model_report_valid
0.365385	0.365385	0.382784	0.371118	glove_tune_ab_model_report_valid
0.519231	0.519231	0.519643	0.492773	glove_tune_xgb_model_report_valid
0.365385	0.365385	0.341963	0.351474	glove_tune_dtc_model_report_valid
0.480769	0.480769	0.466828	0.422205	transformer_tune_rf_model_report_valid
0.500000	0.500000	0.483087	0.455114	transformer_tune_ab_model_report_valid
0.346154	0.346154	0.315934	0.319096	transformer_tune_gb_model_report_valid
0.461538	0.461538	0.446016	0.433572	transformer_tune_xgb_model_report_valid

	Date	News
0	2019-01-06	The tech sector experienced a significant dec...
1	2019-01-13	Sprint and Samsung plan to release 5G smartph...
2	2019-01-20	The U.S. stock market declined on Monday as c...
3	2019-01-27	The Swiss National Bank (SNB) governor, Andre...
4	2019-02-03	Caterpillar Inc reported lower-than-expected ...
5	2019-02-10	The Dow Jones Industrial Average, S&P 500, an...
6	2019-02-17	This week, the European Union's second highes...
7	2019-02-24	This news article discusses progress towards ...
8	2019-03-03	The Dow Jones Industrial Average and other ma...
9	2019-03-10	Spotify, the world's largest paid music strea...
10	2019-03-17	The United States opposes France's digital se...
11	2019-03-24	Facebook's stock price dropped more than 3% o...
12	2019-03-31	This news article reports that the S&P 500 In...
13	2019-04-07	Apple and other consumer brands, including LV...
14	2019-04-14	In March, mobile phone shipments to China dro...
15	2019-04-21	The chairman of Taiwan's Foxconn, Terry Gou, ...
16	2019-04-28	Taiwan's export orders continued to decline f...
17	2019-05-05	Spotify reported better-than-expected Q1 reve...

	Date	News	Key Events	model_response_parsed
0	2019-01-06	The tech sector experienced a significant dec...	{\n "Week End Date": "January 11, 201...	{'Week End Date': 'January 11, 2019', 'Week Po...
1	2019-01-13	Sprint and Samsung plan to release 5G smartph...	{\n "Week End Date": "2019-02-02",\n ...	{'Week End Date': '2019-02-02', 'Week Positive...
2	2019-01-20	The U.S. stock market declined on Monday as c...	{\n "Week End Date": "2019-01-21",\n ...	{'Week End Date': '2019-01-21', 'Week Positive...
3	2019-01-27	The Swiss National Bank (SNB) governor, Andre...	{\n "Week End Date": "2019-02-15",\n ...	{'Week End Date': '2019-02-15', 'Week Positive...
4	2019-02-03	Caterpillar Inc reported lower-than-expected ...	{\n "Week End Date": "2023-03-11",\n ...	{'Week End Date': '2023-03-11', 'Week Positive...

	Week End Date	Week Positive Events	Week Negative Events	Weeks
0	January 11, 2019	[{'Title': 'Roku Inc offers premium video chan...	[{'Title': 'Apple cut its fiscal first quarter...	NaN
1	2019-02-02	[{'Title': 'Sprint and Samsung plan to release...	[{'Title': 'Geely forecasts flat sales for 201...	NaN
2	2019-01-21	[{'Title': 'Netflix raised the prices of its s...	[{'Title': 'The U.S. stock market declined on ...	NaN
3	2019-02-15	[{'Title': 'IBM's stock price increased after ...	[{'Title': 'The Dow, S&P 500, and Nasdaq exper...	NaN
4	2023-03-11	[{'Title': 'Apple reported stronger-than-expec...	[{'Title': 'Caterpillar Inc reported lower-tha...	NaN

Problem Statement¶

Business Context¶

Problem Definition¶

Data Dictionary¶

Please read the instructions carefully before starting the project.¶

Installing and Importing Necessary Libraries¶

Loading the dataset¶

Data Overview¶

Exploratory Data Analysis¶

Univariate Analysis¶

Label¶

Open¶

High¶

Low¶

Close¶

Volume¶

News¶

Open, Close, High and Low distribution¶

Date¶

Bivariate Analysis¶

Open Vs Date¶

High Vs Date¶

Low Vs Date¶

Close Vs Date¶

Volume Vs Date¶

High Vs Open¶

Low Vs Open¶

Close Vs Open¶

Volume Vs Open¶

News Vs Open¶

Low Vs High¶

Close Vs High¶

Volume Vs High¶

News Vs High¶

Close Vs Low¶

Volume Vs Low¶

News Vs Low¶

Volume Vs Close¶

News Vs Close¶

News Vs Volume¶

Open Vs High Vs Low Vs Close Vs Volume Vs News_Length¶

Data Preprocessing¶

Word Embeddings¶

Using Word2Vec¶

Using GloVe¶

Using Sentence Transformer¶

Sentiment Analysis¶

Comment on which metric to use and why¶

Build ML model with Word2Vec¶

DecisionTreeClassifier¶

RandomForestClassifier¶

AdaBoostClassifier¶

GradientBoostingClassifier¶

XGBClassifier¶

Build ML model with GloVe¶

DecisionTreeClassifier¶

RandomForestClassifier¶

AdaBoostClassifier¶

GradientBoostingClassifier¶

XGBClassifier¶

Build ML model with Sentence Transformer¶

DecisionTreeClassifier¶

RandomForestClassifier¶

AdaBoostClassifier¶

GradientBoostingClassifier¶

XGBClassifier¶

Tune ML model with Word2Vec¶

DecisionTreeClassifier¶

RandomForestClassifier¶

AdaBoostClassifier¶

GradientBoostingClassifier¶

XGBClassifier¶

Tune ML model with GloVe¶

DecisionTreeClassifier¶

RandomForestClassifier¶

AdaBoostClassifier¶

GradientBoostingClassifier¶

XGBClassifier¶

Tune ML model with Sentence Transformer¶

DecisionTreeClassifier¶